Maximum Likelihood Estimation for Sample Surveys
In recent years, likelihood methods are increasingly being used to analyze survey data in various disciplines, including the medical, biological, environmental, and social sciences. This book presents an overview of likelihood methods for the analysis of survey data obtained using methods that may r...
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| Main Authors | , , , |
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| Format | eBook |
| Language | English |
| Published |
Milton
Chapman and Hall/CRC
2012
CRC Press LLC CRC Press |
| Edition | 1 |
| Series | Monographs on statistics and applied probability |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In recent years, likelihood methods are increasingly being used to analyze survey data in various disciplines, including the medical, biological, environmental, and social sciences. This book presents an overview of likelihood methods for the analysis of survey data obtained using methods that may result in the sample differing from the population, providing all necessary background material on likelihood inference. The book provides coverage of a range of data types, including multilevel data. Illustrated by numerous worked examples using tractable models, it also addresses advanced topics such as combining data, non-response, and informative sampling. |
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| AbstractList | In recent years, likelihood methods are increasingly being used to analyze survey data in various disciplines, including the medical, biological, environmental, and social sciences. This book presents an overview of likelihood methods for the analysis of survey data obtained using methods that may result in the sample differing from the population, providing all necessary background material on likelihood inference. The book provides coverage of a range of data types, including multilevel data. Illustrated by numerous worked examples using tractable models, it also addresses advanced topics such as combining data, non-response, and informative sampling. Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to biased and inefficient estimates. Maximum Likelihood Estimation for Sample Surveyspresents an overview of likelihood methods for the analysis of sample survey data that account for the selection methods used, and includes all necessary background material on likelihood inference. It covers a range of data types, including multilevel data, and is illustrated by many worked examples using tractable and widely used models. It also discusses more advanced topics, such as combining data, non-response, and informative sampling. The book presents and develops a likelihood approach for fitting models to sample survey data. It explores and explains how the approach works in tractable though widely used models for which we can make considerable analytic progress. For less tractable models numerical methods are ultimately needed to compute the score and information functions and to compute the maximum likelihood estimates of the model parameters. For these models, the book shows what has to be done conceptually to develop analyses to the point that numerical methods can be applied. Designed for statisticians who are interested in the general theory of statistics, Maximum Likelihood Estimation for Sample Surveysis also aimed at statisticians focused on fitting models to sample survey data, as well as researchers who study relationships among variables and whose sources of data include surveys. |
| Author | Welsh, Alan Wang, Suojin Chambers, Raymond L. Steel, David G. |
| Author_xml | – sequence: 1 givenname: Raymond L. surname: Chambers fullname: Chambers, Raymond L. – sequence: 2 givenname: David G. surname: Steel fullname: Steel, David G. – sequence: 3 givenname: Suojin surname: Wang fullname: Wang, Suojin – sequence: 4 givenname: Alan surname: Welsh fullname: Welsh, Alan |
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| Copyright | 2012 by Taylor & Francis Group, LLC |
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| Keywords | Size Biased Sampling Cutoff Sampling Maximum Pseudolikelihood Score Function Inclusion Probabilities Sample Inclusion Probabilities Maximum Likelihood zTi Approximate Maximum Likelihood Estimate Maximum Pseudo-likelihood Unit Nonresponse Minimal Sufficient Statistic Survey Variable Maximum Pseudo-likelihood Estimate Conditional Expectation Missing Information Principle PSUs Item Nonresponse Maximum Sample Likelihood Finite Population Parameters Sample Survey Data CCS Auxiliary Variable Pseudolikelihood Estimate Saddlepoint Approximation |
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| Snippet | In recent years, likelihood methods are increasingly being used to analyze survey data in various disciplines, including the medical, biological,... Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used... |
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| SubjectTerms | Sampling (Statistics) Statistical methods Surveys |
| TableOfContents | Front Cover -- Dedication -- Contents -- Preface -- 1. Introduction -- 2. Maximum likelihood theory for sample surveys -- 3. Alternative likelihood-based methods for sample survey data -- 4. Populations with independent units -- 5. Regression models -- 6. Clustered populations -- 7. Informative nonresponse -- 8. Maximum likelihood in other complicated situations -- Notation |
| Title | Maximum Likelihood Estimation for Sample Surveys |
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