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http://hdl.handle.net/1942/11385
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DC Field | Value | Language |
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dc.contributor.author | SOTTO, Cristina | - |
dc.contributor.author | BEUNCKENS, Caroline | - |
dc.contributor.author | MOLENBERGHS, Geert | - |
dc.contributor.author | Kenward, Michael G. | - |
dc.date.accessioned | 2010-12-29T08:39:49Z | - |
dc.date.available | NO_RESTRICTION | - |
dc.date.available | 2010-12-29T08:39:49Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | COMPUTATIONAL STATISTICS & DATA ANALYSIS, 55(1). p. 301-311 | - |
dc.identifier.issn | 0167-9473 | - |
dc.identifier.uri | http://hdl.handle.net/1942/11385 | - |
dc.description.abstract | The analysis of incomplete longitudinal data requires joint modeling of the longitudinal outcomes (observed and unobserved) and the response indicators. When non-response does not depend on the unobserved outcomes, within a likelihood framework, the missingness is said to be ignorable, obviating the need to formally model the process that drives it. For the non-ignorable or non-random case, estimation is less straightforward, because one must work with the observed data likelihood, which involves integration over the missing values, thereby giving rise to computational complexity, especially for high-dimensional missingness. The stochastic EM algorithm is a variation of the expectation-maximization (EM) algorithm and is particularly useful in cases where the E (expectation) step is intractable. Under the stochastic EM algorithm, the E-step is replaced by an S-step, in which the missing data are simulated from an appropriate conditional distribution. The method is appealing due to its computational simplicity. The SEM algorithm is used to fit non-random models for continuous longitudinal data with monotone or non-monotone missingness, using simulated, as well as case study, data. Resulting SEM estimates are compared with their direct likelihood counterparts wherever possible. (C) 2010 Elsevier B.V. All rights reserved. | - |
dc.description.sponsorship | The authors gratefully acknowledge financial support from the Interuniversity Attraction Pole Research Network P6/03 of the Belgian Government (Belgian Science Policy). | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.rights | © 2010 Elsevier B.V. All rights reserved | - |
dc.subject.other | EM algorithm; Markov chain Monte Carlo; Multivariate Dale model | - |
dc.subject.other | EM algorithm: Markov chain Monte Carlo; multivariate Dale model | - |
dc.title | MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 311 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 301 | - |
dc.identifier.volume | 55 | - |
local.format.pages | 11 | - |
local.bibliographicCitation.jcat | A1 | - |
dc.description.notes | [Sotto, Cristina; Beunckens, Caroline; Molenberghs, Geert] Univ Hasselt, Ctr Stat, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, Interuniv Inst Biostat & Stat Bioinformat, B-3000 Louvain, Belgium. [Kenward, Michael G.] London Sch Hyg & Trop Med, Med Stat Unit, London WC1, England. geert.molenberghs@uhasselt.be | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1016/j.csda.2010.04.026 | - |
dc.identifier.isi | 000283017900026 | - |
item.validation | ecoom 2011 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Restricted Access | - |
item.fullcitation | SOTTO, Cristina; BEUNCKENS, Caroline; MOLENBERGHS, Geert & Kenward, Michael G. (2011) MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness. In: COMPUTATIONAL STATISTICS & DATA ANALYSIS, 55(1). p. 301-311. | - |
item.contributor | SOTTO, Cristina | - |
item.contributor | BEUNCKENS, Caroline | - |
item.contributor | MOLENBERGHS, Geert | - |
item.contributor | Kenward, Michael G. | - |
crisitem.journal.issn | 0167-9473 | - |
crisitem.journal.eissn | 1872-7352 | - |
Appears in Collections: | Research publications |
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sotto 1.pdf Restricted Access | Published version | 402.03 kB | Adobe PDF | View/Open Request a copy |
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