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
Full text not available from this repository.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 |
| Divisions: | Statistics |
| Subjects: | H Social Sciences > HA Statistics |
| Date Deposited: | 31 Oct 2011 14:15 |
| Last Modified: | 11 Sep 2025 06:02 |
| URI: | http://eprints.lse.ac.uk/id/eprint/39208 |
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