Features
Provides an overview of likelihood methods for the analysis of data from sample surveys
Includes necessary background material on likelihood methods
Covers a range of data types, including independent data, multilevel data, and more
Presents advanced topics such as combining data, informative non-response, and informative sample selection
Offersworked examples using tractable and widely used models

Summary
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 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, 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.

Table of 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 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