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Variable selection for regression estimation in finite populations

Nascimento, Silva and Skinner, Chris J. (1997) Variable selection for regression estimation in finite populations. Survey Methodology, 23 (1). pp. 23-32. ISSN 1492-0921

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Abstract

The selection of auxiliary variables is considered for regression estimation in finite populations under a simple random sampling design. This problem is a basic one for model-based and model-assisted survey sampling approaches and is of practical importance when the number of variables available is large. An approach is developed in which a mean squared error estimator is minimised. This approach is compared to alternative approaches using a fixed set of auxiliary variables, a conventional significance test criterion, a condition number reduction approach and a ridge regression approach. The proposed approach is found to perform well in terms of efficiency. It is noted that the variable selection approach affects the properties of standard variance estimators and thus leads to a problem of variance estimation.

Item Type: Article
Official URL: http://www.statcan.gc.ca/ads-annonces/12-001-x/ind...
Additional Information: © 1997 Minister of Industry, Canada
Library of Congress subject classification: H Social Sciences > HA Statistics
Sets: Departments > Statistics
Rights: http://www.lse.ac.uk/library/usingTheLibrary/academicSupport/OA/depositYourResearch.aspx
Date Deposited: 31 Oct 2011 11:49
URL: http://eprints.lse.ac.uk/39196/

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