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  Statistics

  Application of Hierarchical Linear Model \t(HLM) to assess players’ performance

  March 2012

  Application of Hierarchical Linear Model \t(HLM) to assess players’ performance

  R. L. Paige

  A. A. Trindade

  Extensions of Saddlepoint-Based Bootstrap Inference With Application to the First Order \tMoving Average Model

  Abstract\nBinary data classification is an integral part in cyber-security

  as most of the response variables follow a binary nature. The accuracy of data classification depends on various aspects. Though the data classification technique has a major impact on classification accuracy

  the nature of the data also matters lot. One of the main concerns that can hinder the classification accuracy is the availability of noise. Therefore

  both choosing the appropriate data classification technique and the identification of noise in the data are equally important. The aim of this study is bidirectional. At first

  we aim to study the influence of noise on the accurate data classification. Secondly

  we strive to improve the classification accuracy by handling the noise. To this end

  we compare several classification techniques and propose a novel noise removal algorithm. Our study is based on the collected data about online credit-card transactions. According to the empirical outcomes

  we find that the noise hinders the classification accuracy significantly. In addition

  the results indicate that the accuracy of data classification depends on the quality of the data and the used classification technique. Out of the selected classification techniques

  Random Forest performs better than its counterparts. Furthermore

  experimental evidence suggests that the classification accuracy of noised data can be improved by the appropriate selection of the sizes of training and testing data samples. Our proposed simple noise-removal algorithm shows higher performance and the percentage of noise removal significantly depends on the selected bin size.

  Attribute Noise

  Classification Technique

  and Classification Accuracy

  N. G. J. Dias

  K. H. Kumara

  Practical Issues in the development of TTS and SR for the Sinhala Language

  Autoregressive Moving Average Models Under Exponential Power Distributions

  R. W. Barnard

  A. A. Trindade

  Featuring recent advances in the field

  this new textbook presents probability and statistics

  and their applications in stochastic processes. This book presents key information for understanding the essential aspects of basic probability theory and concepts of reliability as an application. The purpose of this book is to provide an option in this field that combines these areas in one book

  balances both theory and practical applications

  and also keeps the practitioners in mind.

  Probability

  Statistics and Stochastic Processes for Engineers and Scientists

  M. Indralingm

  An analysis of the Prevailing Statistics Education in Sri Lanka

  A. A. Trindade

  Problem of Non-Monotone Quadratic Estimating Equations in Saddlepoint Approximating the Moving Average Model of Order One

  Detecting stealthy attacks: Efficient monitoring of suspicious activities on computer networks

  Abstract\n----------\nPlayer classification in the game of cricket is very important

  as it helps the\ncoach and the captain of the team to identify each player’s role in the team and\nassign responsibilities accordingly. The objective of this study is to classify allrounders into one of the four categories in one day international (ODI) Cricket\nformat and to accurately predict new all-rounders’. This study was conducted\nusing a collection of 177 players and ten player-related performance indicators.\nThe prediction was conducted using three machine learning classifiers

  namely\nNaive Bayes (NB)

  k-nearest neighbours (kNN)

  and Random Forest (RF).\nAccording to the experimental outcomes

  RF indicates significantly better\nprediction accuracy of 99.4%

  than its counter parts

  Classification of All-Rounders in the Game of ODI Cricket: Machine Learning Approach

  A. A. Trindade

  Saddlepoint Approximating the distributions of estimators of a Moving Average Model

  N. G. J. Dias

  K H. Kumara

  An Interactive Sinhala medium courseware for teaching secondary school statistics

  N. G. J. Dias

  K. H. Kumara

  MBROLA formatted \tdiphone database for Sinhala Language

  Abstract\n\nNaïve Bayes (NB) is a well-known probabilistic classification algorithm. It is a simple but efficient algorithm with a wide variety of real-world applications

  ranging from product recommendations through medical diagnosis to controlling autonomous vehicles. Due to the failure of real data satisfying the assumptions of NB

  there are available variations of NB to cater general data. With the unique applications for each variation of NB

  they reach different levels of accuracy. This manuscript surveys the latest applications of NB and discusses its variations in different settings. Furthermore

  recommendations are made regarding the applicability of NB while exploring the robustness of the algorithm. Finally

  an attempt is given to discuss the pros and cons of NB algorithm and some vulnerabilities

  with related computing code for implementation.

  Naive Bayes: applications

  variations and vulnerabilities: a review of literature with code snippets for implementation.

  N. G. J. Dias

  K. H. Kumara

  Translation of a given \tsimple English sentence into its equivalent in Sinhala with synthetic sound

  Abstract\nLog-Linear Models (LLMs) are important techniques used in categorical data analysis. Though there are some available published work about LLMs

  the explanation of model building process and the theoretical background are not adequate. Furthermore

  research about the application of the LLM theory and the selection procedure of the best model are handful. Therefore

  this manuscript aims to fill that vacuum. At first

  the construction of LLM and Hierarchical Log-Linear Models (HLLMs)

  a branch of LLMs are discussed in connection with both 2 × 2 and 2 × 2 × 2 contingency tables. Secondly

  an application is presented to analyze the collected data set about the academic performance of elementary students. The manuscript also discusses the criteria to select the best model that fits the collected data.

  Log-linear Models: Construction and Application in Accessing Academic Performance

  A. A. Trindade

  Approximating the unit roots probabilities of the estimator of first order moving average model

  Abstract\nFactors contributing to winning games are imperative

  as the ultimate objective in a game is victory. The aim of this study was to identify the factors that characterize the game of cricket

  and to investigate the factors that truly influence the result of a game using the data collected from the Champions Trophy cricket tournament. According to the results

  this cricket tournament can be characterized using the factors of batting

  bowling

  and decision-making. Further investigation suggests that the rank of the team and the number of runs they score have the most significant influence on the result of games. As far as the effectiveness of assigning bowlers is concerned

  the Australian team has done a fabulous job compared to the rest of the teams.\n

  Characterization of the result of one day format of cricket

  A. A. Trindade

  Saddlepoint Approximating the \tdistributions of estimators of a MA(1) Model

  Experienced Assistant Professor with a demonstrated history of working in the higher education industry. Skilled in Statistics and Data Analytics. Strong education professional with a focused in Mathematics and Statistics.

  Indika

  Prairie View A&M University

  University of Kelaniya

  Texas Tech University

  Eastern New Mexico University

  Prairie View

  TX

  Assistant Professor

  Prairie View A&M University

  Graduate Student / Instructor

  Lubbock

  Texas Area

  Texas Tech University

  Dalugama

  Kelaniya

  Sri Lanka

  Lecturer

  University of Kelaniya

  Portales

  NM

  Assistant Professor

  Eastern New Mexico University

  Doctor of Philosophy (PhD)

  Mathematical Statistics

  Texas Tech University

  First Class

  B.Sc (Sp)

  Mathematics

  University of Kelaniya Sri Lanka

  MS

  Statistics

  Texas Tech University

  MSc

  Operational Research

  University of Moratuwa