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Regression analysis of complex survey data with missing values of a covariate

Skinner, Chris J. and Coker, O. (1996) Regression analysis of complex survey data with missing values of a covariate. Journal of the Royal Statistical Society: Series A (Statistics in Society), 159 (2). pp. 265-274. ISSN 0964-1998

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Abstract

Incomplete observations with missing values of a covariate may be incorporated into the fitting of a linear regression model by maximum likelihood methods. This paper considers the extension of these methods to accommodate a complex sampling design. Point estim- ators are weighted within a pseudomaximum likelihood framework. Standard errors are estimated by a jackknife method. The approach is applied to the fitting of a linear regres- sion model to data from the British Household Panel Survey, where the response variable is a measure of stress and the covariate with missing values is income.

Item Type: Article
Official URL: http://www.blackwellpublishing.com/journal.asp?ref...
Additional Information: © 1996 Wiley-Blackwell
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 14:15
URL: http://eprints.lse.ac.uk/39208/

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