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Regression Analysis for Complex Survey Data with Missing Values of a Covariate
Author(s) -
Skinner C. J.,
Coker O.
Publication year - 1996
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.2307/2983173
Subject(s) - covariate , missing data , statistics , regression analysis , regression , econometrics , mathematics , computer science
SUMMARY 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.