The Theory of Probability is a major tool that can be used to explain and understand the various phenomena in different natural, physical and social sciences. This book provides a systematic exposition of the theory in a setting which contains a balanced mixture of the classical approach and the modern day axiomatic approach. After reviewing the basis of the theory, the book considers univariate distributions, bivariate normal distribution, multinomial distribution and convergence of random variables. Difficult ideas have been explained lucidly and have been augmented with explanatory notes, examples and exercises. The basic requirement for reading this book is simply a knowledge of mathematics at graduate level. This book tries to explain the difficult ideas in the axiomatic approach to the theory of probability in a clear and comprehensible manner. It includes several unusual distributions including the power series distribution that have been covered in great detail. Readers will find many worked-out examples and exercises with hints, which will make the book easily readable and engaging. The author is a former Professor of the Indian Statistical Institute, India.
This is a comprehensive exposition of survey sampling useful both to the students of statistics for the course on sample survey and to the survey statisticians and practitioners involved in consultancy services, marketing, opinion polls, and so on. The text offers updated review of difficult classical techniques of survey sampling, besides covering prediction-theoretic approach of survey sampling and nonsampling errors. NEW TO THIS EDITION Two new chapters—Nonparametric Methods of Variance Estimation (Chapter 19) and Analysis of Complex Surveys (Chapter 20)—have been added. These would greatly benefit the readers. KEY FEATURES Covers concepts of unequal probability sampling. Provides problems of making inference from finite population using tools of classical inference. Describes nonsampling errors including Randomised Response Techniques. Gives over 70 worked-out examples and more than 120 problems and solutions. Supplies live data from India and Sweden—in examples and exercises. What the Reviewer says: This is a very comprehensive modern text on survey sampling with a strong slant towards theoretical results. The book is an excellent reference book and would be a good graduate level sampling text for a course with an emphasis on sampling theory. — JESSE C. ARNOLD, Virginia Polytechnic Institute and State University
This textbook presents a classical approach to some techniques of multivariate analysis in a simple and transparent manner. It offers clear and concise development of the concepts; interpretation of the output of the analysis; and criteria for selection of the methods, taking into account the strengths and weaknesses of each. With its roots in matrix algebra, for which a separate chapter has been added as an appendix, the book includes both data-oriented techniques and a reasonable coverage of classical methods supplemented by comments about robustness and general practical applicability. It also illustrates the methods of numerical calculations at various stages.This self-contained book is ideal as an advanced textbook for graduate students in statistics and other disciplines like social, biological and physical sciences. It will also be of benefit to professional statisticians.The author is a former Professor of the Indian Statistical Institute, India.
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.
Cryptands, soon after their introduction in early 1970s, have proven enormously useful in several areas of chemistry, biology, and materials science. This is continuously growing and venturing into newer fields of supramolecular chemistry research. There is no other book available that attempts to explore all aspects of cryptand chemistry. This book provides a good account of synthetic methods for different types of cryptands, especially chiral cryptands, which remain mostly unexplored. Using the cryptand cavity for homogeneous catalysis, reversible fluorescence sensing, FRET, optically nonlinear materials and construction of molecular level photonic devices – are all discussed. This book also gives an account of using cryptands for a new generation of amphiphiles for Langmuir-Blodgett films and stable vesicles besides the stabilization of metal nanoparticles. This book will be useful for senior university students interested in supramolecular chemistry, as well as budding researchers in this area.
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.
The theory of estimating functions plays a major role in analysis of data pertaining to Biostatistics, Econometrics, Time Series Analysis, Reliability studies and other varied fields. This book discusses at length the application of the theory in interpretation of results in Survey Sampling.
This Book Provides A Comprehensive Account Of Survey Sampling Theory In Fixed Population Approach And Model Based Approach. After Making A Critical Review Of Different Results In Fixed Population Set Up It Shows How Super Population Models Can Be Exploited To Produce Optimal And Robust Sampling Strategies, Specially In Large Scale Sample Surveys. The Central Theme Of The Book Is The Use Of Super Population Models In Making Inference From Sample Surveys. The Book Also Gives Suitable Emphasis On Different Practical Aspects, Like Choice Of Sampling Designs, Variance Estimation, Different Replication And Resampling Procedures.The Author Has Taken Care To Presuppose Nothing More On The Part Of The Reader Than A First Course In Statistical Inference, Sampling Theory And Regression Analysis. He Has Systematically Arranged The Main Results, Supplied Short Proofs, Examples, Explanatory Notes And Remarks And Indicated Research Areas. The Book Will Be Very Useful To Researchers. The Survey Practitioners Will Also Find Some Part Of The Book Very Helpful.
This is a comprehensive exposition of survey sampling useful both to the students of statistics for the course on sample survey and to the survey statisticians and practitioners involved in consultancy services, marketing, opinion polls, and so on. The text offers updated review of difficult classical techniques of survey sampling, besides covering prediction-theoretic approach of survey sampling and nonsampling errors. NEW TO THIS EDITION Two new chapters—Nonparametric Methods of Variance Estimation (Chapter 19) and Analysis of Complex Surveys (Chapter 20)—have been added. These would greatly benefit the readers. KEY FEATURES Covers concepts of unequal probability sampling. Provides problems of making inference from finite population using tools of classical inference. Describes nonsampling errors including Randomised Response Techniques. Gives over 70 worked-out examples and more than 120 problems and solutions. Supplies live data from India and Sweden—in examples and exercises. What the Reviewer says: This is a very comprehensive modern text on survey sampling with a strong slant towards theoretical results. The book is an excellent reference book and would be a good graduate level sampling text for a course with an emphasis on sampling theory. — JESSE C. ARNOLD, Virginia Polytechnic Institute and State University
This textbook presents a classical approach to some techniques of multivariate analysis in a simple and transparent manner. It offers clear and concise development of the concepts; interpretation of the output of the analysis; and criteria for selection of the methods, taking into account the strengths and weaknesses of each." "This book is ideal as an advanced textbook for graduate students in statistics and other disciplines like social, biological and physical sciences. It will also be of benefit to professional statisticians." --Book Jacket.
The theory of estimating functions plays a major role in analysis of data pertaining to Biostatistics, Econometrics, Time Series Analysis, Reliability studies and other varied fields. This book discusses at length the application of the theory in interpretation of results in Survey Sampling.
The Theory of Probability is a major tool that can be used to explain and understand the various phenomena in different natural, physical and social sciences. This book provides a systematic exposition of the theory in a setting which contains a balanced mixture of the classical approach and the modern day axiomatic approach. After reviewing the basis of the theory, the book considers univariate distributions, bivariate normal distribution, multinomial distribution, convergence of random variables and elements of stochastic process. Difficult ideas have been explained lucidly and augmented with explanatory notes, examples and exercises. The basic requirement for reading the book is the knowledge of mathematics at graduate level.This book tries to explain the difficult ideas in axiomatic approach to the theory in a clear and comprehensive manner. It addresses several unusual distributions including the power series distribution. Readers will find many worked-out examples and exercises with hints, which will make the book easily readable and engaging.The author is a former professor of the Indian Statistical Institute, India.
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