Title
A nonparametric approach to weighted estimating equations for regression analysis with missing covariates A nonparametric approach to weighted estimating equations for regression analysis with missing covariates
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Faculty of Medicine and Health Sciences
Publication type
article
Publication
Amsterdam ,
Subject
Mathematics
Computer. Automation
Source (journal)
Computational statistics and data analysis. - Amsterdam
Volume/pages
56(2012) :1 , p. 100-113
ISSN
0167-9473
ISI
000295436200009
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
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.
E-info
https://repository.uantwerpen.be/docman/iruaauth/c66b36/56a2136.pdf
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