Maximum Likelihood Estimation for Sample Surveys

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A01=Alan Welsh
A01=David G. Steel
A01=Raymond L. Chambers
A01=Suojin Wang
advanced survey estimation techniques
Approximate Maximum Likelihood Estimate
Author_Alan Welsh
Author_David G. Steel
Author_Raymond L. Chambers
Author_Suojin Wang
auxiliary
Auxiliary Variable
Category=JMB
Category=PBT
CCS
Clustered populations
conditional
Conditional Expectation
Cutoff Sampling
data
distribution
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
estimating
Finite Population Parameters
function
inclusion
Inclusion Probabilities
informative sampling
Item Nonresponse
likelihood inference methods
Maximum Likelihood
Maximum likelihood in other complicated situations
Maximum likelihood theory for sample surveys
Maximum Pseudo-likelihood
Maximum Pseudo-likelihood Estimate
Maximum Pseudolikelihood
Maximum Sample Likelihood
Minimal Sufficient Statistic
Missing Information Principle
multilevel modeling
nonresponse bias statistics
Populations with independent units
probabilities
Pseudolikelihood Estimate
PSUs
regression analysis surveys
Regression models
Saddlepoint Approximation
Sample Inclusion Probabilities
Sample Survey Data
score
Score Function
Size Biased Sampling
survey data analysis
Survey Variable
Unit Nonresponse
variables
zTi

Product details

  • ISBN 9781584886327
  • Weight: 680g
  • Dimensions: 156 x 234mm
  • Publication Date: 02 May 2012
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

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

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