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

Publication type

Journal Article


Publication date

June 1, 1996


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



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