Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/271
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | AERTS, Marc | - |
dc.contributor.author | CLAESKENS, Gerda | - |
dc.contributor.author | HENS, Niel | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.date.accessioned | 2004-08-31T11:07:40Z | - |
dc.date.available | 2004-08-31T11:07:40Z | - |
dc.date.issued | 2002 | - |
dc.identifier.citation | Biometrika, 89(2). p. 375-388 | - |
dc.identifier.issn | 0006-3444 | - |
dc.identifier.uri | http://hdl.handle.net/1942/271 | - |
dc.description.abstract | Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions both about the distribution of the data and about underlying regression relationships.If such parametric assumptions do not hold, the multiply imputed data are not appropriate and might produce inconsistent estimators and thus misleading results. In this paper, a fully nonparametric and a semiparametric imputation method are studied, both based on local resampling principles. It is shown that the final estimator, based on these local imputations, is consistent under fewer or no parametric assumptions. Asymptotic expressions for bias, variance and mean squared error are derived, showing the theoretical impact of the different smoothing parameters. Simulations illustrate the usefulness and applicability of the method. | - |
dc.language.iso | en | - |
dc.publisher | OXFORD UNIV PRESS | - |
dc.rights | Copyright Biometrika Trust 2002 | - |
dc.subject | Non and semiparametric methods | - |
dc.subject | Computer intensive | - |
dc.subject | Missing data | - |
dc.subject.other | bootstrap; Kernel weight; missing value; multiple imputation; nonparametric imputation; nonresponse; semiparametric imputation | - |
dc.title | Local multiple imputation | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 388 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 375 | - |
dc.identifier.volume | 89 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1093/biomet/89.2.375 | - |
dc.identifier.isi | 000176520500009 | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
item.validation | ecoom 2003 | - |
item.contributor | AERTS, Marc | - |
item.contributor | CLAESKENS, Gerda | - |
item.contributor | HENS, Niel | - |
item.contributor | MOLENBERGHS, Geert | - |
item.fullcitation | AERTS, Marc; CLAESKENS, Gerda; HENS, Niel & MOLENBERGHS, Geert (2002) Local multiple imputation. In: Biometrika, 89(2). p. 375-388. | - |
crisitem.journal.issn | 0006-3444 | - |
crisitem.journal.eissn | 1464-3510 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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a.pdf Restricted Access | Published version | 134.52 kB | Adobe PDF | View/Open Request a copy |
a.pdf | Peer-reviewed author version | 135.51 kB | Adobe PDF | View/Open |
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