Maximum Likelihood Estimation for Sample Surveys

ISBN: 9781584886327 出版年：2012 页码：374 Chambers, Raymond L Steel, David G Wang, Suojin CRC Press

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 likelihood-based methods for sample survey data Introduction Pseudo-likelihood 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 Non-Gaussian 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 Non-Gaussian 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