Institute of Applied Statistics, Sri Lanka www.iassl.lk Featured Segments NEWS IN BRIEF ANNOUNCEMENTS Institute of Applied Statistics Sri Lanka The Professional Center 275/75 Prof. Stanley Wijesundara Mawatha Colombo 07 Sri Lanka +94 11 2588291 [email protected] http://www.iassl.lk http://www.facebook.com/iassl2020/ https://www.linkedin.com/company/iassl/ ISSN 1391 4395 INAUGURATION OF THE DIPLOMA IN APPLIED STATISTICS PROGRAM 2024 A COMPREHENSIVE GUIDE TO ARIMA MODELING STATISTICAL CONSULTANCY: DEALING WITH RESEARCHERS AND RESEARCH SUPERVISORS MACROECONOMIC NOWCASTING WITH HIGH-FREQUENCY DATA IASSL ANNUAL AWARDS CEREMONY 2024 THE UPCOMING INTERNATIONAL STATISTICS CONFERENCE (ISC) 2024: CALLING FOR PAPERS EXPLORING THE CIRCULAR WORLD: UNLOCKING INSIGHTS BEYOND THE LINEAR REALM 12TH ANNUAL GENERAL MEETING 2024 “To consult the statistician after an experiment is finished is often merely to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.” - R. A. Fisher JAN -APRIL 2024 PUZZLE COMPETITION NATIONAL STATISTICS POSTER COMPETITION 2024 CALLING FOR APPLICATIONS PRE CONFERENCE WORKSHOP OPEN FOR REGISTRATIONS SRI LANKAN JOURNAL OF APPLIED STATISTICS CALL FOR PAPERS
From IASSL President’s pen Dr. Niroshan Withanage President/IASSL ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 2 www.iassl.lk JAN -APRIL 2024 It is with great pleasure that I share this message for the current issue of the IASSL newsletter. I would like to take this opportunity to thank all the members who participated in the 12th AGM of IASSL, placing their trust in me and electing me once again as the president of IASSL. The current growth of the IASSL is truly a collective effort of all the past presidents and chairpersons of the subcommittees, and I am grateful for their dedication and hard work. Thanks to their tremendous efforts, we have achieved remarkable growth in recent terms. I look forward to your continued support this year as we strive to reach the highest potential for IASSL. Recognizing the importance of education and training, we have commenced the 11th batch of the Diploma in Applied Statistics (DAS) Program. This year, we received more than 100 applications, a testament to our academic achievements, and ultimately selected around 65 students for the DAS program. For the first time in our history, we have enrolled over 50 students in the DAS program, showcasing the recognition our institution has achieved in education. Additionally, we have successfully conducted island-wide surveys, further demonstrating our commitment to excellence. I would like to take this opportunity to thank the council members, chairpersons of subcommittees, and their members for their unwavering support. This year will be particularly significant for our institute as we host our triennial international statistics conference on the 28th and 29th of December. I extend an invitation to local and foreign scholars to present your latest research and engage with renowned international and local researchers. Furthermore, I would like to thank the American Statistical Association for their generous monetary contribution towards the success of the upcoming conference. Thank you for your continued support. I eagerly anticipate the remarkable advancements we will achieve in the coming months. Warm regards, Dr. Niroshan Withanage President, IASSL
Editorial Dr. Isuru Hewapathirana Editor/IASSL ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 3 www.iassl.lk CONTACT INFORMATION Institute of Applied Statistics Sri Lanka The Professional Center 275/75 Prof. Stanley Wijesundara Mawatha Colombo07 Sri Lanka +94 11 2588291 facebook.com/iassl2020 linkedin.com/company/iassl/ [email protected] Editorial Board: Dr. Hsanthi Pathberiya (Associate editor) Prof. Chandima Tilakaratne Prof. S. Samita Prof. Vasana Chandrasekera Dr. Ursla S Peiris Dr. Jagath Senaratne Dr. Deshani Kariyawasan JAN -APRIL 2024 It is indeed a great honour to be the Editor of IASSL, and I am immensely pleased to launch this first issue of the newsletter for 2024. We have packed this issue with a variety of content for you. In this issue, you’ll find articles from a senior academic, an industry professional in the field of Statistics, and a Statistics undergraduate. Additionally, we are excited to feature a role play titled “A Comprehensive Guide to ARIMA Modeling” that provides an engaging and informative exploration of this statistical method. This issue further includes a “News in Brief” section covering all events of IASSL during the newsletter’s period, such as the 12th Annual General Meeting (AGM) of IASSL, the IASSL Annual Awards Ceremony 2024, and the inauguration of the Diploma in Applied Statistics Program 2024. We highlight the winners of the National Statistics Olympiad 2023 and the Best Research Awards 2023 under the IASSL Annual Awards Ceremony 2024 segment. We also recount various workshops, certificate courses, projects, and activities conducted by IASSL, as well as members’ achievements, awards, fellowships, and book publications from January 1st, 2024 to April 30th, 2024. As usual, we have included the puzzle competition for all readers to enjoy and compete for prizes, and the winners of the previous issue’s puzzle competition are announced in this issue. Finally, we list the upcoming events of IASSL for your information. A huge thank you to all the senior academics, industry professionals, IASSL members, and undergraduates who contributed valuable articles to this issue. I also appreciate the support extended by the President, Secretary, all subcommittee chairpersons, and executive council members of IASSL in providing information about the events conducted during the period from January to April 2024. Last, I would like to thank the editorial board members of IASSL for their immense support in creating this issue of the IASSL newsletter. I invite all readers to submit articles and news for consideration in Issue 2, 2024 of the IASSL newsletter ([email protected]), and I hope you all enjoy reading this issue. Regards, Editor/IASSL
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 4 www.iassl.lk JAN -APRIL 2024 A comprehensive guide to ARIMA Modeling Dr. Hasanthi Pathberiya Associate Editor - IASSL Senior Lecturer Department of Statistics, University of Sri Jayewardenepura The following discussion took place between a senior undergraduate (S) and a junior undergraduate (J). J: I was going through my stats homework on time series analysis, and I'm feeling a bit overwhelmed. There are so many factors to consider. Have you done this before? S: Yeah, I worked on a time series project last semester. It can be a lot at first, but once you break it down, it's not too bad. What specifically are you struggling with? J: I guess it's more about understanding what key things I need to focus on when analyzing time series data. It's a bit confusing. S: I totally get that. Let's start with the basics. First, you need to identify the trend. That's the long-term increase or decrease in the data. It helps to see the overall direction the data is moving in. J: Got it. So, like the upward trend in sales over several years, right? S: Exactly. Next, there can be seasonality. This refers to regular patterns or wave-like fluctuations in the data that repeat over a known, fixed period, like weekly, monthly, or quarterly. J: Right, like how ice cream sales spike every summer. S: Exactly. Then there are cyclic patterns, which are similar to seasonality but don’t follow a fixed calendar pattern. And also, it is not a repeating pattern. These cycles are often influenced by economic conditions or other factors. J: Hmm, like the business cycle with its periods of expansion and recession? S: Precisely. Another important aspect is stationarity. A time series is stationary if its statistical properties, like mean and variance, are constant over time. Many statistical methods require the data to be stationary. J: So if my data are not stationary, Do I need to make it stationary before applying those methods?
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 5 www.iassl.lk JAN -APRIL 2024 S: Yes. J: But, how can I make a non-stationary series as a stationary series? S: You can do that through transformations like differencing or logarithms. Also, don't forget about autocorrelation, which measures how a data point at one time is related to a data point at another time. It's crucial for understanding the internal structure of the data. J: I've heard of autocorrelation. Isn’t that what the autocorrelation function (ACF) is for? S: Exactly. The ACF and the partial autocorrelation function (PACF) help you identify the lag relationships in your data. They’re essential for building ARIMA(p,d,q) models. J: ARIMA(p,d,q) models! Those sound intimidating. S: They can be, but they’re just a combination of autoregressive (AR), integrated (I), and moving average (MA) components. Let's break down how to identify the parameters for each part. J: That would be great. Where do we start? S: First, let's look at the integrated part, I. You have to start by checking for stationarity using statistical tests like the Augmented Dickey-Fuller (ADF) test. If the series is stationary, then d is zero and data may follow an AR(p) model or MA(q) model or ARMA(p,q) model. J: But, if the data are non-stationary? S: Then you have to start by differencing the data. The number of differences needed to make the series stationary is denoted by d. If the series becomes stationary after one differencing, then d is 1. If it takes two differences, then d is 2, and so on. J: Okay. So d is decided from differencing to achieve stationarity? S: Exactly. You can use the ARIMA(p,d,q) model to represent non-stationary data. J: Got it. Then, how can we decide p and q? S: ACF of AR(p) shows an exponentially decaying pattern. To identify p in AR(p), you examine the PACF of data. The point where the PACF cuts off gives you the order of the autoregressive part. If the PACF shows significant spikes up to lag k and then cuts off, p is k. J: Okay, so I just look at where the PACF stops having significant spikes?
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 6 www.iassl.lk JAN -APRIL 2024 S: That’s right. Next is the MA(q) model. PACF of MA(q) shows an exponentially decaying pattern. To identify q in MA(q), you look at the ACF of the data. Similar to the PACF in AR(p), the point where the ACF plot cuts off gives you the order of the moving average part. If the ACF has significant spikes up to lag m and then cuts off, q is m. J: Got it. So, p in AR(p) from the PACF, q in MA(q) from the ACF. That's not as complex as I thought. What about ARMA(p,q)? Can we see cut offs in both ACF and PACF after some lag? S: I thought you'd see it that way, But that's not right. For instance, if both the ACF and PACF are tailing, this suggests an ARMA(1,1) process. J: Really? S: Yes. It can be difficult to find the order p and q of an ARMA process by looking at the ACF and PACF. playing around with different combinations of p and q with a grid search is the usual way to find out the most appropriate orders in ARMA model. J: It makes more sense now. S: It can be a bit tricky, but once you get the hang of it, it’s quite systematic. J: Thanks my friend! This detailed explanation really helps. I feel much more confident about tackling my homework now. S: No worries. Happy to help! If you need any more pointers, just let me know.
Institute of Applied Statistics, Sri Lanka Page 7 www.iassl.lk ISSN 1391 4395 IASSL NEWSLETTER JAN -APRIL 2024 P. Dias B.Sc. Special (Math.) (USJP), PG. Dip. (Stat.) (SL), M.Sc. (Stat.) (Aus.) Senior Lecturer Department of Statistics, Faculty of Applied Sciences, University of Sri Jayewardenepura. Statistical consultancy: Dealing with researchers and research supervisors Most of the researchers have now realized that it is essential to justify their conclusions statistically, at least when publishing in reputed journals. However, most of them are not harnessing the full potential of necessary statistical support in their studies due to lack of understanding of the proper use of statistics. Some of my personal experiences, which I gained during my tenure, are sharedhere. Researchers usually consult statisticians in four stages of their studies. Stage 1: Planning stage of the study (Before collecting the data) Stage 2: Just after collecting the data Stage 3: After analyzing the data Stage 4: After making conclusions Though it is advisable to consult a statistician at stage 1, most of the researchers do it at the other stages. If a researcher consults a statistician at stage 1, then the statistician can help the researcher to ensure the following aspects. Identify the data to be collected to achieve the intended objectives Design the survey or the experiment Identify the statistical techniques to be used in the data analysis Identify limitations and possible alternatives of the statistical techniques to be used Identify a suitable statistical software to be used in the analysis of the data Familiarize with the statistical software to be used Interpret outputs to be obtained Identify the most appropriate way of reporting results After having a clear idea about the above points, a researcher can proceed smoothly in the journey of achieving his/her objectives. However, the researchers who consult a statistician at the other stages of the study, are going to miss almost all of these crucial steps in achieving valid conclusions from their research. Many researchers consult a statistician at the stage 2 (i.e., just after collecting the data). Unfortunately, even many research supervisors believe that their students should consult thestatistician at this stage. Researchers usually meet the statistician and say that “I have collected thedata and now I want to apply statistics to my data”. Most of the researchers are even reluctant tomention about the objectives of the study. They simply want to get some tables and charts tocomplete their study. Some of the following points related to the data collection may prevent thestatistician from helping the researcher in generating valid conclusions from the collected data.
Institute of Applied Statistics, Sri Lanka Page 8 www.iassl.lk ISSN 1391 4395 IASSL NEWSLETTER JAN -APRIL 2024 Data collected are not matching with the objectives Data on some important variables are missing (Sometimes, response variable itself is missing) Some quantitative variables are recorded in ordinal scale (e.g., instead recording age, age group is recorded) The sample may not be a representative one or design used is not appropriate Insufficient sample size Insufficient number of replicates No replicates to identify interaction effects of variables Bias in data collection Very low response rates (Bigger sample size is not the solution for this problem) The researchers who consult at stage 3 used to say that “I have done the statistical analysis. But I do not know how to interpret the results”. If the researcher has an idea about the statistical technique that he/she has used, he/she should be able to interpret the results. In most of these analyses, we may find the following flaws, in addition to the errors in data collection. Data cleaning part is missing (There may be outliers in the analyzed data) Statistical techniques used are not matching with the type of the data (e.g., Calculating Pearson’s correlation coefficient to measure strength of the association between two nominal variables) Assumptions of the statistical techniques are not tested (e.g., use of parametric tests when nonparametric alternatives are to be used) Due to the above reasons, statisticians may not be in a position to interpret the researcher’s results. Before interpreting, it is necessary to check the data quality and appropriateness of selection and application of the statistical technique. Researchers who consult the statistician at stage 4 used to say that “I have made the conclusions from the data and I want to justify them using statistics”. These researchers are not interested in objectives or conclusions of their research. They just want to prove what they believe. One particular example is that they always want to reject the null hypothesis and conclude the alternative. Most of them want to get the conclusions similar to those in the literature. They do not realize that conclusion against the literature is also important. The data may not support what they believed at the starting point of the study. They have to accept the ground reality and find the reasons for conclusions, which are different from the literature, which may lead to new research. In conclusion, we can say that most of the researchers and research supervisors are not aware of the proper use of statistical consultancy. Therefore, it is necessary to popularize proper statistical consultancy among researchers and research supervisors. Hopefully, Institute of Applied Statistics can play an important role in this context.
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 9 www.iassl.lk JAN -APRIL 2024 Mr. R. M. G. S. Krishantha, ACMA, CGMA, AIB Econometrician and Senior Banker Macroeconomic Nowcasting with High-frequency Data Forecasting or Nowcasting Among the statistical terminology, forecasting is the most likely cornerstone, providing insights into the unfolded future. Yet, the time horizon of the ‘future’ has been a topic of discussion over the years. When it comes to the shortest horizon, the current state or probably the immediate future is now called ‘nowcasting’, a term borrowed from the realm of meteorology. The concept of nowcasting has gained traction among economists and policymakers, especially in the aftermath of the 2007-2008 Global Financial Crisis, that pondered the requirement for collecting and analyzing unconventional data such as high-frequency and high-volume data, including financial transactions, google trends, social media data, and mobile phone data, which provide granular and enhanced insights that serve as early detection indicators. Further, with the COVID-19 pandemic, most of the conventional data collection processes such as surveys became ineffective, emphasizing the importance of using unconventional data. For example, rather than monitoring the economic activities with traditional national accounts estimates, Google mobility data and satellite readings of air pollution provide a more updated set of information that can gauge the real-time developments as well. Most empirical models fitted with traditional lower-frequency data lack the ability to foresee imminent risks as they are meant to focus on longer-term perspectives. On the contrary, nowcasts provide up-to-date insights on current economic conditions, especially in times of high economic volatility or crisis, guiding policymakers and businesses to take immediate responses. These nowcast are updated regularly as new data is available, thereby resulting more dynamic and accurate near-term estimates. Moreover, these ‘early estimates’ also could be fed to the more longer-term focused structured or semi-structured models, helping to improve the accuracy of those forecasts. Bridging the data with mixed frequencies Bridging links high-frequency data (same or different frequencies) with lower-frequency data. This helps the researchers to construct models to predict lower-frequency endogenous variables using higher-frequency exogenous variables. A typical example is predicting the monthly inflation. Usually, the official compilers publish the inflation rate at the end of a month. However, most of the data is available on the daily basis and many stakeholders who hold the price data with higher frequencies could use the bridge equations to predict the monthly inflation. The Gross Domestic Product (GDP) is another key economic variable where the bridging is commonly used to predict.
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 10 www.iassl.lk JAN -APRIL 2024 The GDP is estimated by official compilers on a quarterly and annual basis with an unavoidable time lag, as it takes time to gather the necessary data for the estimation. For example, the lag in Sri Lanka is around two and a half months. However, before that, many key indicators, mostly available on a monthly basis, such as the Index of Industrial Production, Purchasing Managers’ Indices, production data of major agriculture crops, including tea, rubber, and coconut, cement availability, imports and exports data, retail sales, and electricity data, are available. The bridging models are ideal under such circumstances to predict quarterly GDP. As in any model, the data preprocessing should first be completed, including missing value imputation, treatments for outliers, transformations, and seasonal adjustment. If the latest values of some of the high-frequency exogenous variables are unavailable, estimating them before bridging is preferred since having a balanced dataset simplifies the analysis and improves the performance of many modelling algorithms. This also enables the model to predict the slowmoving variable with the full set of available high-frequency variables. The estimation could either be judgmental, incorporating the researcher's expertise or employ statistical techniques such as the Autoregressive Integrated Moving Average family of models. Temporal aggregation and interpolation are the most common techniques used in dealing with mixed frequency data. Temporal aggregation transforms high-frequency data to low frequency by calculating sums/averages/last value of the high-frequency variables, in line with the frequency period of the low-frequency data (eg: from monthly to quarterly). However, aggregating data over longer time intervals can lead to a loss of granularity and important short-term variations and trends. Further, aggregation can mask seasonal effects that occur at shorter time scales and can introduce artificial correlations between variables that do not exist at shorter time scales. These drawbacks may lead to biased or inconsistent parameter estimates in the ultimate model, demanding more sophisticated mitigation strategies to improve the robustness. On the other hand, temporal interpolation disaggregates the low-frequency data into highfrequency (eg: quarterly data into monthly data) using mathematical functions such as Chow-Lin, Denton, Splines, etc. Broadly, the Chow-Lin method is a regression-based technique between low and high frequency variables, the Denton method minimizes the adjustments needed to reconcile interpolated high-frequency data with initial low-frequency data and Splines interpolates creating smooth curves by polynomial functions. However, interpolation methods have an inherent risk of high model dependence; displaying artificial patterns that may not be present in the original data, possible collinearity problems, the interpolated high-frequency data may be less volatile compared to the true data, general interpolation methods may not adequately capture advanced dynamics present within the data, etc. Further, there are more sophisticated methods such as state-space form, a mathematical representation used to model dynamic systems, capturing their internal state and how they evolve over time due to inputs and disturbances. In case of mixed frequencies, different state equations will be specified for different frequencies and the output equation incorporates information of all
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 11 www.iassl.lk JAN -APRIL 2024 the frequencies. However, these are computationally demanding when frequency mismatches are large and complex when the model is not linear. Nowcasting approaches After conducting necessary transformations, the mixed-frequency data can be used for nowcasting using different modeling approaches, such as bridge, mixed data sampling (MIDAS) and Factor Models, or even techniques such as machine learning and Vector Autoregression (VAR). The following discusses direct bridge, MIDAS and mixed-frequency Factor Models that are commonly for nowcasting. Direct Bridging The bridge models aggregate high-frequency data (e.g: monthly indicators) to match the frequency of the low-frequency target variable (e.g: quarterly GDP) and use the aggregated values in a regression model to link them. Here, the inclusion of exogenous variables/indicators is not based on causal relations, but on the statistical fact that they contain timely updated information. If the data of high-frequency variables are unavailable over the remainder of the low-frequency tenure (eg: quarter), they had to be estimated beforehand, usually based on univariate time series models, and aggregated to obtain their corresponding quarterly values. Here, =1 to represents aggregated short-term indicators and t = 1 to T is the time in long-term tenure. As the Bridge equation is linear, Ordinary Least Squares estimation is optimal (That is OLS is the Best Linear Unbiased Estimator). Mixed Data Sampling (MIDAS) Approach MIDAS is an extension of the basic Bridge model that allows for the direct inclusion of highfrequency data in a regression framework without the aggregation. Instead of aggregating the high-frequency data, MIDAS models use polynomial distributed lags to capture the influence of high-frequency observations over multiple periods. MIDAS is designed to prevent possible parameter proliferation when frequency mismatch is large by using a polynomial weighting scheme enabling to describe the potentially large number of lagged high-frequency observations through small number of parameters. Therefore, MIDAS provides a tightly parameterized reduced-form regression that involves processes sampled at different frequencies. The approach of generating low-frequency variables from higher frequency variables is called “split” or “skip” sampling. The split sampling is where segmenting the high-frequency data into periods that align with the low-frequency variable of interest. For example, if there is monthly 0
data, data from each month is sampled as a different variable, making three exogenous variables if the target variable is in quarterly basis. Since, split sampling allows for the utilization of all available high-frequency data within each segmented period, it is advantageous in capturing the dynamics. However, when the number of input variables increase when the frequency is high, making it difficult to execute. MIDAS solves this problem using the polynomial weighting scheme. Further, unless it is properly managed, split sampling could lead to overfitting if the model captures noise or idiosyncrasies specific to each segment rather than general trends. On the other hand, skip sampling is where selecting a specific sequence of months to create the exogenous series. For example, select the last month of the quarter to the sample, skipping the first two months. Since skip sampling involves fewer data, it possesses the risk of data loss but can be computationally less intensive. A basic specification of this approach is as follows. Where ‘m’ is the frequency and is a lagged distribution. There are several extensions of MIDAS, such as MIDAS-AR that introduces autoregression into the specification, unrestricted MIDAS (U-MIDAS) that omits the tight parameterization in MIDAS and attempts to estimate all the possible parameters under general linear dynamic framework which conditions the parameters of the underlying high-frequency model, etc. Mixed Frequency Factor Models Extracting the underlying factors of a dataset and inclusion of factors into the mixed frequency framework is advantageous when the number of high frequency variables/indicators is too large for models to address the parameter proliferation issue, even for MIDAS. The idea is to firstly reduce the number of variables to a smaller set (factors) that contains as much as information/variability that the original data set had. Hence, factor models decompose the behaviour of a vector of economic variables into a component driven by a few unobservable factors that are common to all the variables but with specific effects on them and idiosyncratic component. The Dynamic Factor Model (DFM) is a commonly used technique that represent the evolution of time series in terms of a reduced number of unobserved common factors which evolve over time. For example, the business cycle is an unobservable common factor that drives real GDP growth, tax receipts, retail sales, etc. A DFM expresses an ×1 vector of observed time series variables as a ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 12 www.iassl.lk JAN -APRIL 2024
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 13 www.iassl.lk JAN -APRIL 2024 function of a smaller number of unobserved (or latent) factors and a mean-zero idiosyncratic component . () and Ψ() are lag polynomial matrices, where the th lag polynomial ()is called the dynamic factor loading for the th series ,. Moreover, rather than allowing the model to determine the underlying factor, the researcher can employ any known dimension reduction techniques like Principal Components, Three-Pass Regression Filtering and Kalman Filter at the first stage and then employ the nowcast model to the reduced number of factor series. The advantages of factor models include, enhanced predictive power and improve the robustness of estimates by filtering out noise and focusing on systematic components of the data, reduction in the dimensionality, reduced multicollinearity, guard against omitted variable bias, and some robustness in the presence of noise, non-fundamental shocks and structural breaks. However, interpreting the model is difficult and may not have clear economic meanings since the exogenous variables are underlying unobservable series. Invitation to send noteworthy members' achievements to publish in MAY 2024 - AUG 2024 NEWSLETTER... Please forward, - Newsworthy achievements made by IASSL members during May 2024 - August 2024: Local/international awards, scholarships, Local/international research grants, Promotions and educational qualifications (MPhil and above). Please send a brief description. - Books/book chapters published by IASSL members during May 2023 - August 2023. Please send a brief description and a photo of the cover page of the book.
Ms. Nimsara Dissanayaka Fourth Year Student, B. Sc. (Honours in Statistics) Department of Statistics and Computer Science Faculty of Science, University of Peradeniya. circumference of the unit circle and circular distributions do not have a true zero value. 2. Key Concepts in Circular Statistics Let us consider a linear variable ‘time of a day’ and transform it into a circular variable (θ) as follows: where θ is the angular measurement, T is the total period (24 hours), and t is the number of time units on the linear measurement scale. A circular variable cannot be expressed simply in terms of θ. Instead, it must be represented using the trigonometric functions cos(θ) and sin(θ) to maintain periodicity. Circular Mean (or Directional Mean): Unlike the arithmetic mean in linear data, the circular mean calculates the average direction of circular data. It considers the circular nature of the data, ensuring that the mean direction lies within the range of 0 to 360 degrees. Accordingly, the circular mean can be calculated as follows: ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 14 www.iassl.lk JAN -APRIL 2024 Exploring the Circular World: Unlocking Insights Beyond the Linear Realm In the realm of statistics, we often deal with data that is linear, following a straight path from one point to another. However, there exists another dimension of data that doesn't conform to such a linear path but rather moves in cycles or circles. Imagine you are analysing data on wind directions, where the measurements range from 0 to 360 degrees. Traditional statistical methods may falter here because they assume a linear progression from 0 to 360, failing to capture the cyclical nature of the data. This is where circular statistics comes into play, offering specialised tools to analyse and interpret such circular data effectively. 1. What is Circular Statistics? Circular statistics, also known as directional statistics, is a branch of statistics that deals with data measured along a circle or periodic interval. It is widely used in various fields such as meteorology, biology, geography, and even psychology. It offers several benefits over linear statistics, including its ability to maintain the cyclical nature of data, analyse periodic patterns and trends, and compare circular data across different groups or time periods. The key feature of circular data is that the endpoints are connected, forming a closed loop or cycle. Circular statistics accepts the curvature of cyclical data, recognising that 0 to 360 degrees form a continuous loop rather than a finite endpoint. In circular statistics, data can be represented as points on the
where and for observations (Jammalamadaka & Sengupta, 2001). Mean Resultant Vector: For a given circular variable, the mean resultant vector can be calculated as, where and is a measure of angular dispersion ranging from 0 to 1. The value 0 indicates uniform dispersion and 1 indicates complete concentration in one direction as shown in Figure 1. Figure 1: distribution of data points with different values. Circular Variance: While traditional variance measures the spread of data around the mean on a linear scale, circular variance takes into account the circular nature of the data, accounting for the shortest distance between two points on a circle. The circular variance is and the circular standard deviation is which are related to directly (Açar, 2023). Checking Correlation for Circular Data: Fisher and Lee (1983) proposed Pearson moment correlation for two circular variables ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 15 www.iassl.lk JAN -APRIL 2024 as, where and denotes the mean direction of the respective circular variables and . The value of the is between -1 and +1, and the correlation of the two circular variables are interpreted based on the value as in the linear case. Testing Uniformity for Circular Data: This involves determining whether the data is evenly distributed around the circle or if there are any significant concentrations or gaps in the distribution. We can use the Rayleigh test for checking the uniformity, which assumes that the distribution has only one mode and that the data is sampled from a von Mises distribution. The Von Mises distribution is also known as the circular normal distribution or Tikhonov distribution, and it is a continuous probability distribution on the circle. Regression Approaches in Modelling Circular Data: Instead of depending only on linear methods, general statistical modelling techniques require that the circular nature of variables be incorporated into the model. As one of the modelling techniques, in regression modelling, the choice of regression type depends on whether the circular variable is the independent or dependent variable. Circular and linear variables frequently exist in the same model. In these situations, circular variables are usually processed using the sine and cosine functions, while linear variables can be included in their original form. The basic formula for regression modelling with a circular variable is
Visualising Circular Data: A variety of visualisation techniques are available that can be used to represent circular data, such as heat maps, rose plots, and circular histograms. These charts provide clear insights into the variables being studied, revealing hidden cyclical trends. Given the circular nature of the data, each data point falls within one complete circle, enriching the visualisation with comprehensive information. Figure 2: Circular histogram of hourly rainfall distribution of each month in Kegalle, Sri Lanka in 2022. This circular histogram shown in Figure 2 illustrates the distribution of hourly rainfall in each month in Kegalle, Sri Lanka in 2022. Each sector denotes the total rainfall for each hour, with distinct colours indicating the monthly order and corresponding total rainfall amount. While the colour filling clearly defines the monthly rainfall distribution, the radius of each sector and the length of its segments offer clear insights into the rainfall amounts. 3. Applications In different fields, circular statistics are used to analyse circular events and might provide valuable insights that linear statistics could overlook. In Meteorology: Weather data, including wind direction, temperature variation over a period of time, and seasonal variations, often show cyclical patterns. Meteorologists can accurately examine and forecast these patterns with the use of circular statistics. For example, knowing which direction the wind is most probably to blow could aid in identifying the best locations for wind farms. Figure 3: Circular histogram of hourly wind direction in Kegalle, Sri Lanka in 2022 The wind direction graph in Figure 3 describes the wind direction frequency in Kegalle, Sri Lanka in 2022. Therefore, as compared to basic plots, circular plots provide clearer insights into circular variables. Figure 4 provides more detailed information about the most probable wind direction in each hour in each season of Sri Lanka in 2022. As can be seen in Figures 3 and 4, we can clearly understand the patterns in wind direction if we treat the wind direction as a circular variable. In Biology: Biological rhythms show cyclical behaviours, such as the circadian cycles found in organisms. Biologists can better understand phenomena such as animal migration patterns, growth cycles, and behavioural patterns influenced by environmental conditions by using circular statistics. ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 16 www.iassl.lk JAN -APRIL 2024
In Economics and Finance: Economic indicators that exhibit cyclical patterns, like Business cycles, stock market fluctuations, and seasonal sales trends, can be examined using circular statistics. Economists can create reliable models for economic research and provide more accurate forecasts through the use of circular statistics. 4. Conclusion Conclusively, the field of circular statistics offers a significant transformation in the way we examine and understand data that demonstrate periodic trends. Circular statistics, which departs from traditional linear approaches, offers specific tools and methods to capture the unavoidable circularity of instances in a range of fields of study, including biology, economics, meteorology, and more. The insights gained by circular statistics will surely drive innovation and grow knowledge across a wide range of fields as we continue to investigate the circular world. References Fisher, N. I., & Lee, A. J. (1983). A Correlation Coefficient for Circular Data. Biometrika, 70(2), 327–332. Açar, T. S. (2023). A new approach in modelling the circular data: circular ridge estimator. Journal of Statistical Computation and Simulation, 93(4), 671-683. Jammalamadaka, S. R., Sengupta, A. (2001). Topics In Circular Statistics (vol 5). Singapore: World Scientific Publishing Company. ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 17 www.iassl.lk JAN -APRIL 2024 Figure 4: Distribution of season-wise hourly maximum occurrence wind direction withmaximum wind speed in each monsoon in Kegalle, Sri Lanka in 2022.
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 18 www.iassl.lk JAN -APRIL 2024 President/ IASSL Dr. Niroshan Withanage Department of Statistics, Faculty of Applied Sciences, USJ. Immediate Past President/ IASSL Dr. Chitraka Wickramarachchi Department of Statistics, Faculty of Applied Sciences, USJ. Vice President/ IASSL Prof. Kapila T. Rathnayake Faculty of Applied Sciences, SUSL. Secretary/ IASSL Dr. Rajitha M. Silva Department of Statistics, Faculty of Applied Sciences, USJ. Treasurer/ IASSL Mrs. DABN Amarasekara Department of Crop Science, Faculty of Agriculture, UOR. Editor/ IASSL Dr. Isuru Hewapathirana Software Engineering Teaching Unit, Faculty of Science, UOK. 12th Annual General Meeting 2024 The 12th Annual General Meeting of the Institute of Applied Statistics Sri Lanka was held on 30th March 2024 at the Auditorium of the Professional Centre (OPA). All the life Members were Invited for the occasion. The New Executive council was appointed at the AGM for the year 2024.
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 19 www.iassl.lk JAN -APRIL 2024 Assistant Secretary Dr. Wasana Wijesuriya Principal Research Officer, Rubber Research Institute of Sri Lanka. Associate Editor Dr. Hasanthi Pathberiya Department of Statistics, Faculty of Applied Sciences, USJ. Chairperson/ Academic Training Committee Dr. Neluka Devpura Department of Statistics, Faculty of Applied Sciences, USJ. Chairperson/ Statistics Popularization Committee Prof. L.D.B. Suriyagoda Department of Crop Science, Faculty of Agriculture, UOP. Chairperson/ R & D Committee Dr. A.P.G.S. De. Silva Former Director, Department of Census and Statistics, Sri Lanka. Executive Council Member Prof. C. D. Tilakaratne Department of Statistics, Faculty of Science, UOC. Executive Council Member Prof. S. Samita Department of Crop Science, Faculty of Agriculture, UOP. Executive Council Member Prof. T. Sivananthawerl Department of Crop Science, Faculty of Agriculture, UOP. AssistantTreasurer Dr. Priyath De Silva Former Vice President / OPA Former Director Investments, Ministry of Nation Bulting.
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 20 www.iassl.lk JAN -APRIL 2024 Ex Officio Department of Census and Statistics, Sri Lanka. Executive Council Member Dr. Chathuri Jayasinghe Department of Statistics, Faculty of Applied Sciences, USJ. Executive Council Member Dr. Bashitha Kavinga Department of Statistics & Computer Science, Faculty of Science, UOK. Executive Council Member Dr. Priyadarshana Dharmawardena Senior Statistician, Department of Census and Statistics, Sri Lanka. Executive Council Member Prof. N. Vasana Chandrasekara Department of Statistics & Computer Science, Faculty of Science, UOK.
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 21 www.iassl.lk JAN -APRIL 2024 1.Dr. Neluka Devpura (Chairperson), Department of Statistics, Faculty of Applied Sciences, USJ. 2.Dr. Priyath De Silva, (Assistant Treasurer) (Ex Officio), Former Vice President OPA. 3.Mr. Sampath Hapuarachchi, Faculty of Applied Sciences, SUSL. 4.Prof. R. Sooriyaarachchi, (Retired Lecturer) UOC. 5.Dr. Thiyanga Talagala, Department of Statistics, Faculty of Applied Sciences, USJ. 6.Dr. Manjula Perera, Department of Statistics, Faculty of Applied Sciences, USJ. 7.Dr. Dushan Kumarathunga, Department of Agricultural Biology, Faculty of Agriculture, UOR. 8.Dr. Shanika Thathsarani, Faculty of Applied Sciences, USJ. 1.Dr. Isuru Hewapathirana (Editor), Software Engineering Teaching Unit, Faculty of Science, UOK. 2.Dr. Hasanthi Pathberiya (Associate Editor), Department of Statistics, Faculty of Applied Sciences, USJ. 3.Prof. N.V. Chandrasekara, Department of Statistics & Computer Science, Faculty of Science, UOK. 4.Prof. C. D. Tilakaratne, Department of Statistics, Faculty of Science, UOC. 5.Prof. S. Samita, Department of Crop Science, Faculty of Agriculture, UOP. 6.Dr. Jagath Senarathne, Department of Statistics & Computer Science, Faculty of Science, UOP 7.Dr. Ursla S Peiris, Biostatistics Unit, Faculty of Livestock, Fisheries & Nutrition, WUSL 8.Dr. Deshani Kriyawasam, Department of Statistics, Faculty of Science, UOC 1.Prof. Kapila Rathnayaka (Vice President/Chairperson), Faculty of Applied Sciences, SUSL. 2.Dr. R. M. Silva (Secretary) (Ex Officio), Department of Statistics, Faculty of Applied Sciences, USJ. 3.Mrs. Nimalshanthi Amarasekara (Treasurer) (Ex Officio), Department of Crop Science, Faculty of Agriculture, UOR. 4.Dr. Priyath De Silva, (Assistant Treasurer) (Ex Officio), Former Vice President OPA. 5.Dr. A. P. G. S. De Silva, (Retired) Department of Census and Statistics, Sri Lanka. 6.Mrs. Padma Yatapana (Retired) Division of Interdisciplinary Studies, Institute of Technology, UOM. 7.Dr. Hasanthi Pathberiya, Department of Statistics, Faculty of Applied Sciences, USJ. 8.Eng. S. Vaikunthan, AMIE (SL). 1.Prof. L. D. B. Suriyagoda (Chairperson), Department of Crop Science, Faculty of Agriculture, UOP. 2.Dr. Priyath De Silva, (Assistant Treasurer) (Ex Officio), Former Vice President OPA. 3.Dr. Rajitha M. Silva, Department of Statistics, Faculty of Applied Sciences, USJ. 4.Dr. Chathuri Jayasinghe, Department of Statistics, Faculty of Applied Sciences, USJ. 5.Prof. T. Sivananthawer, Department of Crop Science, Faculty of Agriculture, UOP. 6.Mr. S. Denny Jasotharan, National Hospital of Sri Lanka. 7.Ms. Navodi Mekhala Hakmanage, Department of Computer Systems Engineering, Faculty of Computing, UOK. 8.Ms. Heshani Achinthika Mendis ( Coordinator Assistant- Msc.in Data science & Artificial Intelligence) 1. Dr. A. P. G. S. De Silva (Chairperson), (Retired) Department of Census and Statistics, Sri Lanka. 2.Dr. Chitraka Wickramarachchi, Department of Statistics, Faculty of Applied Sciences, USJ. 3.Dr. Priyath De Silva, (Assistant Treasurer) (Ex Officio), Former Vice President OPA. 4.Dr. Priyanga Talagala, Department of Computational Mathematics, Faculty of Information Technology, UOM. 5.Mr. D. C. A. Gunawardene, (Retired) Department of Census and Statistics, Sri Lanka. 6.Mr. Nandana Gunarathne, ASMP (The World Bank Project). 7.Prof . Siddhisena, Professor Emeritus, Department of Demography, UOC. 8.Prof . R. Sooriyaarchchi, (Retired) Department of Statistics, Faculty of Science, UOC. 9.Dr. Hasanthi Pathberiya Department of Statistics, Faculty of Applied Sciences, USJ (Invited Member) 10.Ms. Desha Fernando (Invited Member) 11.Ms. Theja Sanduni (Invited Member) Academic Training Committee Editorial Board House & Finance Management Committee Statistics Popularization Committee Research & Delevelopment Committee Subcommittee Members
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 22 www.iassl.lk JAN -APRIL 2024 IASSL Annual Awards Ceremony 2024 The Annual Awards Ceremony of the Institute of Applied Statistics, Sri Lanka was held on 30th March 2024 at the Auditorium of the Professional Centre (OPA). It was a great honor to have Mr. E. A. P. N. Edirisinghe, Conservator General of Forest at the Department of Forest Conservation, grace the Awards ceremony as the distinguished chief guest. Winners of the Best Research Awards competition 2023 and the winners of the National Statistics Olympiad competition 2023 were awarded at the ceremony. The opportunity was given to present the work of the winners of the best research awards competition. All life Members, award winners, Olympiad winners and their parents were too Invited for the occasion.
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 23 www.iassl.lk JAN -APRIL 2024 The Best Research Awards competition was successfully organized by IASSL for the 10th year to encourage and appreciate undergraduates, postgraduates, and researchers who have successfully completed their research studies during the year 2023. Twenty nine (29) undergraduates and nine (09) postgraduates who completed the degree requirements in Sri Lankan Universities have participated in this year’s competition. For the open category, eight (08) research articles that were published in reputed journals or conferences during the period from 1st January 2023 to 31st December 2023 were considered. Best Research Awards 2023
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 24 www.iassl.lk JAN -APRIL 2024 Winner 1st Runner up 2nd Runner up Mr. H. A. D. D. Nadeekantha Mr. R. S. A. U. Dharmarathna Ms. S. P. P. M. Subasinghe Mr. D. S. Sonnadara Mr. M. D. N. Chandrasiri Ms. T. Jayakody Merit Awards Undergraduate Category Ms. N. I. M. B. Senanayaka Ms. J. H. Priyanka Mr. G. N. C. Ariyasingh Ms. G. K. Subasinghe
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 25 www.iassl.lk JAN -APRIL 2024 Winner 1st Runner up 2nd Runner up Ms. P. W. Jeewanthi Ms. V. R. Dunuwila Ms. N. M. Wijesekara Postgraduate Category Open Category Winner 1st Runner up 2nd Runner up Prof. N. P. R. Deyshappriya Dr. I. U. Hewapathirana Mr. S. H. B. Wijekoon
National Statistics Olympiad 2023 The 10th National Statistics Olympiad, organized by the Institute of Applied Statistics Sri Lanka (IASSL), took place on October 29, 2023, as previously reported in the IASSL newsletter. The aim of this competition was to promote an interest in statistics among school and university students nationwide. The National Statistics Olympiad is a key event for IASSL, designed to engage the community and encourage the use of statistics among students across the country. The contest featured intense competition among exceptional young minds. We are pleased to announce that all category winners (Junior and Senior) advanced to the first round of the 14th Statistics Olympiad - 2024, hosted by the C. R. Rao Advanced Institute of Mathematics, Statistics, and Computer Science (AIMSCS) on February 4, 2024. Additionally, we are proud to report that five participants were selected for the second round of the competition, scheduled for May 2024. All prize winners and merit award recipients in both Junior and Senior categories received medals and certificates at the IASSL annual awards ceremony, held on March 30, 2024, at the Professional Centre (OPA) auditorium. ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 26 www.iassl.lk JAN -APRIL 2024
National Statistics Olympiad 2023 ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 27 www.iassl.lk JAN -APRIL 2024 Junior level winners Junior level merit awards Gold Medal D. M. Ovin R. Gamage B/Bandarawela Central College Silver Medal D. M. T. H. Dissanayake Badulla Centrel College Bronze Medal R. Adithya Weerakoon Badulla Centrel College H. Ranod Subasinghe Badulla Centrel College S. D. T. Athsara Senadheera Thurstan College N. Shakya Punchihewa H/ Rajapaksha Central College Senior level winners Gold Medal W. V. Samadhi Chamathka Visakha Vidyalaya, Colombo 05 Silver Medal R. M. Asheni Bandara Visakha Vidyalaya, Colombo 05 Bronze Medal J. K. Nilini Windya Perera Holy Family Balika Maha Vidyalaya, Wennappuwa
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 28 www.iassl.lk JAN -APRIL 2024 Anaisha Choksy Ladies College Colombo 07 G. W. T. H. Sarathchandra Visakha Vidyalaya, Colombo 5 Y. Ediriweera University of Moratuwa S. K. Aluthpatabendige Ananda College, Colombo-10 Supuli Obeysekera Holy Family Convent, Bambalapitiya K. B. H. M. K. D. Bandaranayake Sri Jayewardenepura University S. Y. Mendis Visakha Vidyala, Colombo 5 S. Jeyawaran University of Moratuwa S. Abhyankar Royal College, Colombo S. R. Wickremasinghe University of Colombo V. Uthayasooriyan C.M.S Ladies' College Senior level merit awards
Statistics Olympiad Competition 1st Place Sanghapa Arachchige Sukhithi Chamali University of Colombo 7th Place Gamage Dona Nikini Prabhashi Maheshika Gamage University of Kelaniya 8th Place Madampe Appuhamilage Shashini Chamudika Siriwardhana University of Kelaniya 9th Place Hapugala Arachchige Shanili Vinusha Seneviratne Visakha Vidyalaya , Colombo 10th Place Kavindu Jayodh Abeysinghe Jayawardana University of Colombo 11th Place Aluthge Ashini Madhuwanthi University of Kelaniya 12th Place Merengha Thisuri Tharindi Panditha Jayarathne University of Sri Jayawardenapura 14th Place Sandun Sankalpa Wanniarachchi Ananda College Colombo 10 16th Place Rajith Ekanayaka University of Kelaniya 20th Place Weliwitigoda Thivain Jayuka Wimaladharma Royal College Colombo 07 5th Place Madappuli Arachchige Vikum Dulara Fernando Lyceum International School - Panadura 19th Place Ganeshan Pranavi Wijayaratnam Hindu Central College,Negombo 20th Place Wijesinghe Arachchilage Kalshini Dulmitha Holy Angles Girls' College Kuliyapitiya ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 29 www.iassl.lk JAN -APRIL 2024 Junior level winners Organized by CR Rao Advanced Institute of Mathematics, Statistics and Computer Science (AIMSCS) - 2023 Senior level winners
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 30 www.iassl.lk JAN -APRIL 2024 The inauguration ceremony of the 11th batch of the Diploma in Applied Statistics programme was held on the 06th of April 2024 at the Professional Center (OPA) Auditorium, Prof. Stanley Wijesundera Mawatha, Colombo 07. The Institute of Applied Statistics Sri Lanka (IASSL) offers this one-year Diploma which is equivalent to Level 1 of a B.Sc. Degree in Statistics. Dr. M. A. Wijeratne - Director of the Kahawatte Plantations was invited as the chief guest of this ceremony. Mrs. Carol Jennin Thomas – an alumnus of the 6th batch, the executive council members, ATC members, the teaching panel and the enrolled 58 candidates participated this event.
From 1998, she continued her academic duties in the Department of Statistics and Computer Science of the same university till her retirement. She continued her academic journey at the University of Dortmund, Germany, where she pursued postgraduate studies and obtained her Ph.D. in Statistics in 1990. She has held positions as a Visiting Assistant Professor in the Department of Mathematics and Statistics at Mississippi State University, USA (January 1998-June 1998), a Research Fellowship in Statistics at Dortmund University, Germany, and as an Adjunct Professor in the Department of Mathematics and Statistics at Memorial University, Canada. During her service at the University of Peradeniya, she worked as the Head of the department, Chairperson of the Board of Study in Statistics & Computer Science at the Postgraduate Institute of Science, Chairperson of the Faculty Research Committee and served in many other academic committees to uplift the quality of the university education. Her notable contributions include serving as the Chief Editor of the Sri Lankan Journal of Applied Statistics, Sri Lanka (August 2012 – December 2014), and as Chief Editor for the International Research Congress 2015 at the Postgraduate Institute of Science, Sri Lanka. She has undertaken the role of reviewer and editorial board member for numerous national and international journals. To disseminate knowledge, she organized and coordinated many workshops at the PGIS, and worked as a co-chairperson of two international conferences in statistics organized by the PGIS, by showcasing her leadership in advancing statistical research and education. Prof. Wijekoon was chosen as the "Statistician of the week" of the second week in December in 2013, organized by the American statistical association to commemorate the International Year of Statistics held in 2013, and the organizers published her profile in their website. In May 2023, she retired as a senior professor, and currently she is an Emeritus professor in the same department, and an adjunct professor at National Institute of Fundamental studies. Members' Achievements ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 31 www.iassl.lk JAN -APRIL 2024 We are delighted to announce that two IASSL fellowship has been awarded at the Annual Awards ceremony 2024. One of the fellowships was awarded to Senior Professor Pushpakanthie Wijekoon from the Department of Statistics and Computer Science, University of Peradeniya. She obtained a BSc Special degree in Mathematics in 1982 from the University of Kelaniya, and joined the Department of Mathematics, University of Peradeniya in 1983 as an Assistant lecturer. NEWS IN BRIEF
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 32 www.iassl.lk JAN -APRIL 2024 The other IASSL fellowships was awarded to Professor T. Sivananthawerl from the Department of Crop Science, Faculty of Agriculture, University of Peradeniya. His academic journey includes earning his PhD in Silviculture & Forest Growth Modeling from George August Universität Göttingen, Germany, in 2001. Prior to that, he attained his Master of Science in Management of Natural Resources and Sustainable Agriculture from the Agriculture University of Norway in 1996. He also obtained a M.Sc. in Biostatistics at the Postgraduate Institute of Agriculture in 1994. Professor Sivananthawerl commenced his educational pursuits with a B.Sc in Agriculture from the University of Peradeniya in 1991. He has authored over 44 full research papers, 66 abstracts and 3 book chapters, published in prestigious journals and books. Among his contributions are serving on the technical committee of the Central Environmental Authority for the Central and Sabaragamuwa provinces and consulting for the Food and Agriculture Organization of the United Nations. Additionally, he has overseen numerous postgraduate research endeavors spanning Plantation Silviculture, Forest Growth Modeling, Forest Ecology, Urban Forestry, and Arboriculture Awards Winner of the SLAAS section E1 Mr. Anjana Dharmaratne was awarded as the winner of the SLAAS section E1 for Statistics Research at the B.Sc. Level in 2024. His research is titled as “A Novel Goodness of Fit Test for zero-inflated Poisson Regression Models” and it was supervised by Dr. Roshini Sooriyarachchi (Retd. Professor). Ph.D. in Statistics Dr. Jayani Hapugoda was awarded a Ph.D. in Statistics by the University of Colombo in 2024. Her PhD is titles as “Joint modeling of survival and count data“ and it was supervised by Dr. Roshini Sooriyarachchi (Retd. Professor)
Book Chapter ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 33 www.iassl.lk JAN -APRIL 2024 Book Chapter: Leveraging Artificial Intelligence for Ethical Social Media Influencer Communication. Author: Dr. I. U Hewapathirana, Software Engineering Teaching Unit, Faculty of Science, UOK. Year of Publication: 2024 DOI: https://doi.org/10.4018/979-8-3693-0912-4.ch017 Publisher: IGI Global. Included in: In N. Bi & R. Zhang (Eds.), Global Perspectives on Social Media Influencers and Strategic Business Communication (pp. 343-359). The book chapter is included in the book Global Perspectives on Social Media Influencers and Strategic Business Communication, which delves into influencer research and practices, exploring their impact on various industries and sectors. This book chapter explores the connections between artificial intelligence (AI) and the ethical dimensions of influencer communication on social media. The ethical aspects are evaluated according to the criteria outlined in the Professional Code of Ethics of the Public Relations Society of America (PRSA). The study reviews the multiple aspects of influencer communication, including emerging challenges and legal implications resulting from the continued development of AI in social media. Furthermore, a dataset was collected from the social media platform Reddit, and a case study analysis was performed using the NodeXL software. This empirical investigation aims to investigate social media users' perspectives on specific ethical concerns associated with integrating artificial intelligence (AI). The findings presented in this chapter provide scholars with an advanced understanding of AI capabilities, offer industry professionals valuable guidance for ethical decision-making, and offer lawmakers guidance for developing regulatory frameworks.
Courses conducted by IASSL during JAN - APR 2024 1. Business Analytics using Power BI Survey Design & Analysis. 2.Survey Design & Analysis. Analyzing multivariate time series and modeling volatility using R and EViews. 3. Systematic Literature Review (SLR) with Bibliometric. Analysis a way of manuscript writing with PRISMA. 4. 5. R Essential Training in Learning R. 6. Data Analytics with Python. Become a vital member of IASSL Please Visit https://www.iassl.lk/ ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 34 www.iassl.lk JAN -APRIL 2024 MRS. H. L. K. PEIRIS MRS. S. M. M. LAKMALI DR. G. A.C.N. PRIYADARSHANI MR. R. K. R. K. AMARASINGHE New Life Members MRS. SAZNA FARIZ New Student Members MS. A. P. HATHARASINGHE New Annual Members
ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 35 www.iassl.lk Upcomings JAN -APRIL 2024 Upcoming Workshops (Click to Apply) 1.Business Analytics using Power BI 2.Data Analysis with Python 3.Artificial Neural Networks with R. 4.Tableau for Business Analytics 5.R Essential Training in Data Visualization with R Essential LaTeX Skills for Research Writing using Overleaf 6. 7.Statistical Modeling with R. 8.Business Analysis with Excel Systematic Literature Review (SLR) with Bibliometric Analysis: a way of manuscript writing with PRISMA 9. 10.Structural Equation Modelling [SEM] 11.Basic Statistics for Managers and Researchers 12.Machine Learning with Python
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ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 37 www.iassl.lk Puzzle Competition Please email your submission to [email protected] on or before 15th August 2024. The draw will be held on the 28th August, 2024. Correct submissions will be shortlisted and the winners will be selected randomly and will be announced in the Issue 2 of 2024 IASSL newsletter. JAN -APRIL 2024 Across 4. The part of the population from which we actually collect information. 6. A set of data involving or depending on two variables. 7. A ...... random sample consists of separate simple random samples drawn from groups of similar individuals. 11. Values that divide the data into four equal parts. 12. Groups of similar individuals in a population. 13. The difference between the greatest and the least numbers in the set. 14. The process of drawing a conclusion about the population based on a sample. Down 1. A ...... sample consists of a simple random samples of small groups from a population. 2. The entire group of individuals about which we want information. 3. A ...... event has a probability 1. 5. ...... shows the frequency of data that is in equal intervals. 8. A study in which a treatment is imposed in order to observe a response. 9. Data that is more than 1.5 times the value of the interquartile range beyone the quartiles. 10. The number of times the value appears in the data set.
Thank you FREEDOM BUSINESS SERVICES (PVT) LTD for sponsoring Issue 1 2024 puzzle competition!! ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 38 www.iassl.lk JAN -APRIL 2024 Issue 3 2023 Puzzle Competition winners: 1st Place: K. H. U. Anthony 2nd Place: Buddhika Dissanayake 3rd Place: Mohamed Sabri Isma Lebbe
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ISSN 1391 4395 IASSL NEWSLETTER Institute of Applied Statistics, Sri Lanka Page 43 www.iassl.lk H O W T O B E C O M E A M E M B E R O F I A S S L ? P l e a s e v i s i t h t t p : / / w w w . i a s s l . l k A p p l y f o r t h e M e m b e r s h i p A p p l i c a t i o n f o r m We are updating Contact Information of IASSL Members... Please refer below link to update your details: https://forms.gle/itfDcB5Buedbwp9F7 JAN -APRIL 2024 Click on the post to Go ahead....
WE SINCERELY APPRECIATE ALL WHO CONTRIBUTED TO THIS ISSUE, AND THOSE WHO PARTICIPATED IN THE PREPARATION OF IT. EDITORIAL BOARD/IASSL CONTRIBUTIONS TO THE MAY-AUG (ISSUE 2) 2024 NEWSLETTER: If you have any submissions, comments, suggestions & or feedback, please send them to [email protected]. I A S S L N E W S L E T T E R O f f i c i a l N e w s l e t t e r o f t h e I n s t i t u t e o f A p p l i e d S t a t i s t i c s S r i L a n k a Institute of Applied Statistics Sri Lanka The Professional Center 275/75 Prof. Stanley Wijesundara Mawatha Colombo 07 Sri Lanka +94 11 2588291 [email protected] http://www.iassl.lk http://www.facebook.com/iassl2020/ https://www.linkedin.com/company/iassl / Institute of Applied Statistics, Sri Lanka www.iassl.lk ISSN 1391 4395 IASSL NEWSLETTER JAN -APRIL 2024