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http://hdl.handle.net/1942/8526
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DC Field | Value | Language |
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dc.contributor.author | VERBEKE, Geert | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | BEUNCKENS, Caroline | - |
dc.date.accessioned | 2008-10-13T12:31:48Z | - |
dc.date.available | 2008-10-13T12:31:48Z | - |
dc.date.issued | 2008 | - |
dc.identifier.citation | STATISTICAL SCIENCE, 23(2). p. 201-218 | - |
dc.identifier.issn | 0883-4237 | - |
dc.identifier.uri | http://hdl.handle.net/1942/8526 | - |
dc.description.abstract | Model selection and assessment with incomplete data pose challenges in addition to the ones encountered with complete data. There are two main reasons for this. First, many models describe characteristics of the complete data, in spite of the fact that only an incomplete subset is observed. Direct comparison between model and data is then less than straightforward. Second, many commonly used models are more sensitive to assumptions than in the complete-data situation and some of their properties vanish when they are fitted to incomplete, unbalanced data. These and other issues are brought forward using two key examples, one of a continuous and one of a categorical nature. We argue that model assessment ought to consist of two parts: (i) assessment of a model's fit to the observed data and (ii) assessment of the sensitivity of inferences to unverifiable assumptions, that is, to how a model described the unobserved data given the observed ones. | - |
dc.description.sponsorship | We gratefully acknowledge support from Belgian IUAP/PAI network “Statistical Techniques and Modeling for Complex Substantive Questions with Complex Data.” | - |
dc.format.extent | 580359 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | INST MATHEMATICAL STATISTICS | - |
dc.rights | © Institute of Mathematical Statistics, 2008 | - |
dc.subject.other | interval of ignorance; linear mixed model; missing at random; missing not at random; multivariate normal; sensitivity analysis | - |
dc.subject.other | interval of ignorance; linear mixed model; missing at random; missing not at random; multivariate normal; sensitivity analysis | - |
dc.title | Formal and informal model selection with incomplete data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 218 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 201 | - |
dc.identifier.volume | 23 | - |
local.format.pages | 18 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | Katholieke Univ Leuven, Ctr Biostat, B-3000 Louvain, Belgium. Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1214/07-STS253 | - |
dc.identifier.isi | 000259275400003 | - |
item.fullcitation | VERBEKE, Geert; MOLENBERGHS, Geert & BEUNCKENS, Caroline (2008) Formal and informal model selection with incomplete data. In: STATISTICAL SCIENCE, 23(2). p. 201-218. | - |
item.validation | ecoom 2009 | - |
item.contributor | VERBEKE, Geert | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | BEUNCKENS, Caroline | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
crisitem.journal.issn | 0883-4237 | - |
crisitem.journal.eissn | 2168-8745 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
GeertMolenberghs13.pdf | Peer-reviewed author version | 566.76 kB | Adobe PDF | View/Open |
euclid.ss.1219339113.pdf | Published version | 446.57 kB | Adobe PDF | View/Open |
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