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Journal Article

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

Authors

Publication date

1996

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 estimators 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 regression 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.

Published in

Journal of the Royal Statistical Society Series A (Statistics in Society)

Volume

Vol.159, no.2,265-274

ISSN

16

Subjects

Statistical Analysis and Survey Methodology

Links

http://serlib0.essex.ac.uk/search?/sjournal+of+the+royal+statistical+society/sjournal+of+the+royal+statistical+society/1%2C7%2C13%2CB/exact&FF=sjournal+of+the+royal+statistical+society+series+a+statistics+in+society&1%2C2%2C/indexsort=-

Notes

Albert Sloman Library Periodicals *restricted to Univ. Essex registered users*

#503862


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