Maximum Likelihood Estimation for Sample Surveys

ISBN-13: 9781584886327 / Angielski / Twarda / 2012 / 391 str.

Raymond L. Chambers;Suojin Wang;Alan Welsh

Opis

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. 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.

In recent years, likelihood methods have proven to be useful in methodological and practical advances in various disciplines, including the social sciences and engineering. Likelihood Methods in Sample Surveys presents an overview of likelihood methods for the analysis of sample survey data, providing all necessary background material on likelihood inference. The book provides complete coverage on a range of data types, including multilevel and aggregate data. Illustrated by numerous worked examples and software implementation, it also addresses advanced topics such as combining data, non-response, and informative sampling.

Spis treści

Introduction. Maximum likelihood theory for sample surveys. Alternative likelihood-based methods for sample survey data. Populations with independent units. Regression models. Clustered populations. Informative nonresponse. Maximum likelihood in other complicated situations.

O autorze

Raymond L. Chambers, David G. Steel, Suojin Wang, Alan Welsh