A Mixed Model-assisted Regression Estimator that Uses Variables Employed at the Design Stage

The Generalized regression estimator (GREG) of a finite population mean or total has been shown to be asymptotically optimal when the working linear regression model upon which it is based includes variables related to the sampling design. In this paper a regression estimator assisted by a linear mi...

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Bibliographic Details
Published in:Statistical methods & applications 2006-08, Vol.15 (2), p.139-149
Main Authors: Ranalli, M, Montanari, Giorgio
Format: Article
Language:English
Subjects:
Auxiliary Information
Design based approach
Estimating techniques
Expected values
Finite Population
Optimal estimator
Population
Regression analysis
REML
Simulation
Statistical methods
Studies
Variables
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Summary:The Generalized regression estimator (GREG) of a finite population mean or total has been shown to be asymptotically optimal when the working linear regression model upon which it is based includes variables related to the sampling design. In this paper a regression estimator assisted by a linear mixed superpopulation model is proposed. It accounts for the extra information coming from the design in the random component of the model and saves degrees of freedom in finite sample estimation. This procedure combines the larger asymptotic efficiency of the optimal estimator and the greater finite sample stability of the GREG. Design based properties of the proposed estimator are discussed and a small simulation study is conducted to explore its finite sample performance. [PUBLICATION ABSTRACT]
ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-006-0006-8