Regression Analysis for Complex Survey Data with Missing Values of a Covariate

C. J. Skinner and O. Coker

Journal of the Royal Statistical Society Series A, 1996, vol. 159, issue 2, 265-274

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.

Date: 1996
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2307/2983173

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:159:y:1996:i:2:p:265-274

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X

Access Statistics for this article

Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples

More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().