Publication
Title
A nonparametric approach to weighted estimating equations for regression analysis with missing covariates
Author
Abstract
Missing data often occur in regression analysis. Imputation, weighting, direct likelihood, and Bayesian inference are typical approaches for missing data analysis. The focus is on missing covariate data, a common complication in the analysis of sample surveys and clinical trials. A key quantity when applying weighted estimators is the mean score contribution of observations with missing covariate(s), conditional on the observed covariates. This mean score can be estimated parametrically or nonparametrically by its empirical average using the complete case data in case of repeated values of the observed covariates, typically assuming categorical or categorized covariates. A nonparametric kernel based estimator is proposed for this mean score, allowing the full exploitation of the continuous nature of the covariates. The performance of the kernel based method is compared to that of a complete case analysis, inverse probability weighting, doubly robust estimators and multiple imputation, through simulations. (C) 2011 Elsevier B.V. All rights reserved.
Language
English
Source (journal)
Computational statistics and data analysis. - Amsterdam
Publication
Amsterdam : 2012
ISSN
0167-9473
Volume/pages
56:1(2012), p. 100-113
ISI
000295436200009
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 12.07.2012
Last edited 26.06.2017
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