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＜電子ブック＞
Complex Surveys : Analysis of Categorical Data / by Parimal Mukhopadhyay

版	1st ed. 2016.
出版者	(Singapore : Springer Nature Singapore : Imprint: Springer)
出版年	2016
本文言語	英語
大きさ	XV, 248 p : online resource
著者標目	*Mukhopadhyay, Parimal author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Biometry LCSH:Social sciences -- Statistical methods  全ての件名で検索 FREE:Statistical Theory and Methods FREE:Statistics in Business, Management, Economics, Finance, Insurance FREE:Biostatistics FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
一般注記	Chapter 1. Preliminaries -- Chapter 2. The Design-Effects and Mis-Specification Effects -- Chapter 3. Some Classical Models in Categorical Data Analysis -- Chapter 4. Analysis of Categorical Data under a Full Model -- Chapter 5. Analysis of Categorical Data under Log-Linear Models -- Chapter 6. Analysis of Categorical Data under Logistic Regression Model -- Chapter 7. Analysis in the Presence of Classification Errors -- Chapter 8. Approximate MLE’s from Survey Data 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 surveydesigns 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 HTTP:URL=https://doi.org/10.1007/978-981-10-0871-9

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