Parimal Mukhopadhyay Knihy

Focusing on the axiomatic approach to probability theory, this book systematically presents complex concepts in an accessible manner. It covers univariate and bivariate distributions, multinomial distribution, and convergence of random variables, supplemented with clear explanations, examples, and exercises. The inclusion of unique distributions, such as the power series distribution, enhances its depth. Aimed at readers with graduate-level mathematics knowledge, the text is designed to be engaging and comprehensible, making it a valuable resource for students and professionals alike.

The primary objective of this book is to study some of the research topics in the area of analysis of complex surveys which have not been covered in any book yet. It discusses the analysis of categorical data using three models: a full model, a log-linear model and a logistic regression model. It is a valuable resource for survey statisticians and practitioners in the field of sociology, biology, economics, psychology and other areas who have to use these procedures in their day-to-day work. It is also useful for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. The importance of sample surveys today cannot be overstated. From voters’ behaviour to fields such as industry, agriculture, economics, sociology, psychology, investigators generally resort to survey sampling to obtain an assessment of the behaviour of the population they are interested in. Many large-scale sample surveys collect data using complex survey designs like multistage stratified cluster designs. The observations using these complex designs are not independently and identically distributed – an assumption on which the classical procedures of inference are based. This means that if classical tests are used for the analysis of such data, the inferences obtained will be inconsistent and often invalid. For this reason, many modified test procedures have been developed for this purpose over the last few decades.

The aim of this book is to make a comprehensive review on some of the research topics in the area of survey sampling which has not been covered in any book yet. The proposed book aims at making a comprehensive review of applications of Bayes procedures, Empirical Bayes procedures and their ramifications (like linear Bayes estimation, restricted Bayes least square prediction, constrained Bayes estimation, Bayesian robustness) in making inference from a finite population sampling. Parimal Mukhopadhyay is Professor at the Indian Statistical Institute (ISI), Calcutta. He received his Ph.D. degree in Statistics from the University of Calcutta in 1977. He also served as a faculty member in the University of Ife, Nigeria, Moi University, Kenya, University of South Pacific, Fiji Islands and held visiting positions at University of Montreal, University of Windsor, Stockholm University, University of Western Australia, etc. He has to his credit more than fifty research papers in Survey Sampling, some co-authored, three text books on Statistics and three research monographs in Survey Sampling. He is a member of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute.