Maximum likelihood estimation for sample surveys
 Responsibility
 R. L. Chambers ... [et al.].
 Language
 English.
 Imprint
 Boca Raton, FL : CRC Press, c2012.
 Physical description
 xvi, 375 p. : ill ; 24 cm.
 Series
 Monographs on statistics and applied probability (Series) 125.
Access
Available online
 marc.crcnetbase.com CRCnetBASE
Math & Statistics Library

Stacks

Unknown
QA276.6 .L55 2012

Unknown
QA276.6 .L55 2012
More options
Creators/Contributors
 Contributor
 Chambers, R. L. (Ray L.)
Contents/Summary
 Bibliography
 Includes bibliographical references and indexes.
 Contents

 Introduction Nature and role of sample surveys Sample designs Survey data, estimation and analysis Why analysts of survey data should be interested in maximum likelihood estimation Why statisticians should be interested in the analysis of survey data A sample survey example Maximum likelihood estimation for infinite populations Bibliographic notes Maximum likelihood theory for sample surveys Introduction Maximum likelihood using survey data Illustrative examples with complete response Dealing with nonresponse Illustrative examples with nonresponse Bibliographic notes Alternative likelihoodbased methods for sample survey data Introduction Pseudolikelihood Sample likelihood Analytic comparisons of maximum likelihood, pseudolikelihood and sample likelihood estimation The role of sample inclusion probabilities in analytic analysis Bayesian analysis Bibliographic notes Populations with independent units Introduction The score and information functions for independent units Bivariate Gaussian populations Multivariate Gaussian populations NonGaussian auxiliary variables Stratified populations Multinomial populations Heterogeneous multinomial logistic populations Bibliographic notes Regression models Introduction A Gaussian example Parameterization in the Gaussian model Other methods of estimation NonGaussian models Different auxiliary variable distributions Generalized linear models Semiparametric and nonparametric methods Bibliographic notes Clustered populations Introduction A Gaussian group dependent model A Gaussian group dependent regression model Extending the Gaussian group dependent regression model Binary group dependent models Grouping models Bibliographic notes Informative nonresponse Introduction Nonresponse in innovation surveys Regression with item nonresponse Regression with arbitrary nonresponse Imputation versus estimation Bibliographic notes Maximum likelihood in other complicated situations Introduction Likelihood analysis under informative selection Secondary analysis of sample survey data Combining summary population information with likelihood analysis Likelihood analysis with probabilistically linked data Bibliographic notes.
 (source: Nielsen Book Data)
 Publisher's Summary
 Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using wellestablished 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 Surveys presents 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, nonresponse, 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 Surveys is 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.
(source: Nielsen Book Data)
Subjects
 Subject
 Sampling (Statistics)
Bibliographic information
 Publication date
 2012
 Series
 Monographs on statistics and applied probability ; 125
 Note
 "A Chapman & Hall book."
 ISBN
 9781584886327 (hbk. : acidfree paper)
 1584886323 (hbk. : acidfree paper)