Generalized Linear Models and Extensions

This book presents a wide range of topics to address the needs of several groups of users of rapidly growing methods of generalized linear models. Since the introduction of the idea of generalized linear models (GLM) in early seventies, during the past four decades the modelling of statistical data have experienced a major transformation from linear models based on normality assumption to a more flexible unified approach of generalized linear models. The number of readers and users of generalized linear models have increased manifold. In addition, the use of generalized linear models has expanded in many new fields of applications where statistical models are being employed at an increasing rate. It is important to note here that the learners and users of GLM have a widely varied background in different disciplines. Considering these pressing needs, this book focuses on: (i) upper undergraduate and graduate level students in need of a thorough understanding about the basic concepts of generalized linear models along with appropriate applications; (ii) researchers and users in need of advanced generalized linear models for analysing bivariate or multivariate data stemming from longitudinal or repeated measures data; and (iii) new challenges to analyse big data where the traditional techniques fail to provide any reasonable modelling strategy. In other words, this book starts with a thorough background of the generalized linear models for the new learners, then provides multivariate extensions to advanced level techniques for researchers and users in various disciplines, and finally some innovative modelling strategies are introduced using generalized linear models in the emerging field of big data analytics. It provides materials for new learners, for users/researchers who are in need of more advanced techniques and also strategies for employing linear models in big data analytics. Hence, techniques of generalized linear models will be presented in the proposed book covering the needs of new learners, users of advanced techniques, researchers in need of statistical modelling of any data type and users of big data analytics wanting to increase predictive accuracy of classification and regression tree techniques.

M. Ataharul Islam was a QM Hussain Professor at the Institute of Statistical Research and Training, University of Dhaka, Bangladesh. He was a former Professor of Statistics at the University of Dhaka, University Sains Malaysia, King Saud University and the East West University of Dhaka, Bangladesh. He served as a visiting faculty at the University of Hawaii and University of Pennsylvania. He is a recipient of the Pauline Stitt Award, Western North American Region (WNAR) Biometric Society Award for content and writing, East West Center Honor Award, University Grants Commission Award for book and research, Ibrahim Gold Medal for research, etc. He has published more than 100 papers in international journals on various topics, extensively on longitudinal and repeated measures data, including multistate and multistage hazards models, statistical models for repeated measures data, Markov models with covariate dependence, generalized linear models, conditional and joint models for correlated outcomes. He has authored books with leading publishers, including Springer, on Foundations of Biostatistics, Analysis of Repeated Measures Data, Markov Models, and Reliability and Survival Analysis, and contributed chapters to several others.

Soma Chowdhury Biswas is currently a Professor at the Department of Statistics, University of Chittagong, Bangladesh. She did her graduation and Master of Science (M.Sc.) degrees in Statistics from University of Dhaka, Bangladesh and completed Graduate Diploma (G.D.) and Masters (M.A.) from Australian National University, Canberra, Australia and obtained her PhD from Jamal Nazrul Islam Research Center for Mathematical & Physical Sciences (JNRCMPS), University of Chittagong, Bangladesh. Her areas of interest are bio-statistics, inference, modeling Markov Chain & demography.