Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11881
Title: Inferential Implications of Over-Parametrization: A Case Study in Incomplete Categorical Data
Authors: Poleto, Frederico Z.
Paulino, Carlos D.
Molenberghs, Geert 
Singer, Julio M.
Issue Date: 2011
Publisher: WILEY-BLACKWELL
Source: INTERNATIONAL STATISTICAL REVIEW, 79(1). p. 92-113
Abstract: In the context of either Bayesian or classical sensitivity analyses of over-parametrized models for incomplete categorical data, it is well known that prior-dependence on posterior inferences of nonidentifiable parameters or that too parsimonious over-parametrized models may lead to erroneous conclusions. Nevertheless, some authors either pay no attention to which parameters are nonidentifiable or do not appropriately account for possible prior-dependence. We review the literature on this topic and consider simple examples to emphasize that in both inferential frameworks, the subjective components can influence results in nontrivial ways, irrespectively of the sample size. Specifically, we show that prior distributions commonly regarded as slightly informative or noninformative may actually be too informative for nonidentifiable parameters, and that the choice of over-parametrized models may drastically impact the results, suggesting that a careful examination of their effects should be considered before drawing conclusions.
Que ce soit dans un cadre Bay´esien ou classique, il est bien connu que la surparam´etrisation, dans les mod`eles pourdonn´ees cat´egorielles incompl`etes, peut conduire`a des conclusions erron´ees. Cependant, certains auteurs persistent`an´egliger les probl`emes li´es`alapr´esence de param`etres non identifi´es. Nous passons en revue la litt´erature dans cedomaine, et consid´erons quelques exemples surparam´etr´es simples dans lesquels les´el´ements subjectifs influencentde fac¸on non n´egligeable les r´esultats, ind´ependamment de la taille des´echantillons. Plus pr´ecis´ement, nous montronscomment des a priori consid´er´es comme peu ou non-informatifs peuvent se r´ev´eler extrˆemement informatifs en ce quiconcerne les param`etres non identifi´es, et que le recours`a des mod`eles surparam´etr´es peut avoir sur les conclusionsfinales un impact consid´erable. Ceci sugg`ere un examen tr`es attentif de l’impact potentiel des a priori.
Notes: [Poleto, Frederico Z.; Singer, Julio M.] Univ Sao Paulo, Inst Matemat & Estat, BR-05314970 Sao Paulo, Brazil. [Paulino, Carlos D.] Univ Tecn Lisbon, Inst Super Tecn, P-1049001 Lisbon, Portugal. [Paulino, Carlos D.] CEAUL FCUL, P-1049001 Lisbon, Portugal. [Molenberghs, Geert] Univ Hasselt, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, B-3000 Louvain, Belgium.
Keywords: Contingency table; identifiability; incomplete data; pattern-mixture model; selection model; sensitivity analysis;contingency table; identifiability; incomplete data; pattern-mixture model; selection model; sensitivity analysis
Document URI: http://hdl.handle.net/1942/11881
ISSN: 0306-7734
e-ISSN: 1751-5823
DOI: 10.1111/j.1751-5823.2011.00130.x
ISI #: 000289160400005
Rights: (C) 2011 The Authors. International Statistical Review (c) 2011 International Statistical Institute
Category: A1
Type: Journal Contribution
Validations: ecoom 2012
Appears in Collections:Research publications

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