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Restricted quasi-score estimating functions for sample survey data

Part of: Parametric inference Linear inference, regression

Published online by Cambridge University Press:  14 July 2016

Y.-X. Lin ,
D. Steel  and
R. L Chambers
Y.-X. Lin
Affiliation:
School of Mathematics and Applied Statistics, University of Wollongong, NWS 2522, Australia. Email address: yanxia@uow.edu.au
D. Steel
Affiliation:
School of Mathematics and Applied Statistics, University of Wollongong, NWS 2522, Australia. Email address: dsteel@uow.edu.au
R. L Chambers
Affiliation:
Southampton Statistical Sciences Research Institute, Building 39, University of Southampton, Highfield, Southampton SO17 1BJ, UK. Email address: rc6@soton.ac.uk
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Abstract

This paper applies the theory of the quasi-likelihood method to model-based inference for sample surveys. Currently, much of the theory related to sample surveys is based on the theory of maximum likelihood. The maximum likelihood approach is available only when the full probability structure of the survey data is known. However, this knowledge is rarely available in practice. Based on central limit theory, statisticians are often willing to accept the assumption that data have, say, a normal probability structure. However, such an assumption may not be reasonable in many situations in which sample surveys are used. We establish a framework for sample surveys which is less dependent on the exact underlying probability structure using the quasi-likelihood method.

Keywords

Estimating functionquasi-likelihoodsample survey

MSC classification

Primary: 62J12: Generalized linear models
Secondary: 62F99: None of the above, but in this section
Type
Part 2. Estimation methods
Information
Journal of Applied Probability , Volume 41 , Issue A: Stochastic Methods and their Applications , 2004 , pp. 119 - 130
Copyright
Copyright © Applied Probability Trust 2004 

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