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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14374

Title: A nonparametric approach to weighted estimating equations for regression analysis with missing covariates
Authors: Creemers, An
Aerts, Marc
Hens, Niel
Molenberghs, Geert
Issue Date: 2012
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.
Notes: [Creemers, An] Univ Hasselt, Fac Wetenschappen, B-3590 Diepenbeek, Belgium. [Hens, Niel] Univ Antwerp, CHERMID, Ctr Evaluat Vaccinat, WHO Collaborating Ctr,Vaccine & Infect Dis Inst, B-2020 Antwerp, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, B-3000 Louvain, Belgium. an.creemers@uhasselt.be
URI: http://hdl.handle.net/1942/14374
DOI: 10.1016/j.csda.2011.06.013
ISI #: 000295436200009
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

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