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http://hdl.handle.net/1942/14374
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DC Field | Value | Language |
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dc.contributor.author | CREEMERS, An | - |
dc.contributor.author | AERTS, Marc | - |
dc.contributor.author | HENS, Niel | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.date.accessioned | 2012-11-16T11:24:50Z | - |
dc.date.available | 2012-11-16T11:24:50Z | - |
dc.date.issued | 2012 | - |
dc.identifier.citation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, 56 (1), p. 100-113 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | http://hdl.handle.net/1942/14374 | - |
dc.description.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. | - |
dc.description.sponsorship | This work has been funded by the IAP research network nr P6/03 of the Belgian Government (Belgian Science Policy). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government – department EWI. | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.rights | © 2011 Elsevier B.V. All rights reserved. | - |
dc.subject.other | missing covariates; weighted estimating equations; doubly robustness; mean score estimation; kernel weight | - |
dc.subject.other | Missing covariates; Weighted estimating equations; Doubly robustness; Mean score estimation; Kernel weights | - |
dc.title | A nonparametric approach to weighted estimating equations for regression analysis with missing covariates | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 113 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 100 | - |
dc.identifier.volume | 56 | - |
local.format.pages | 14 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.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 | - |
local.publisher.place | AMSTERDAM | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
local.type.programme | VSC | - |
dc.identifier.doi | 10.1016/j.csda.2011.06.013 | - |
dc.identifier.isi | 000295436200009 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.validation | ecoom 2012 | - |
item.contributor | CREEMERS, An | - |
item.contributor | AERTS, Marc | - |
item.contributor | HENS, Niel | - |
item.contributor | MOLENBERGHS, Geert | - |
item.fullcitation | CREEMERS, An; AERTS, Marc; HENS, Niel & MOLENBERGHS, Geert (2012) A nonparametric approach to weighted estimating equations for regression analysis with missing covariates. In: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 56 (1), p. 100-113. | - |
crisitem.journal.issn | 0167-9473 | - |
crisitem.journal.eissn | 1872-7352 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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creemers 1.pdf Restricted Access | Published version | 336.89 kB | Adobe PDF | View/Open Request a copy |
Creemers et al 2012 (2).pdf | Peer-reviewed author version | 168.55 kB | Adobe PDF | View/Open |
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