BIBCYT Autor: Alejandría BE 7.0.7b0

Autor: =Boos, Dennis D.

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Autor:	Presnell, Brett ; Boos, Dennis D.
Título:	The IOS Test for Model Misspecification
Páginas/Colación:	pp. 216 - 227
Url:	http://miranda.asa.catchword.org/vl=1466172/cl=60/nw=1/rpsv/cw/asa/01621459/v99n465/s21/p216

Journal of the American Statistical Association Vol. 99, no. 465 March 2004

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Resumen

A new test of model misspecification is proposed, based on the ratio of in-sample and out-of-sample likelihoods. The test is broadly applicable and, in simple problems, approximates well-known, intuitive methods. Using jackknife influence curve approximations, it is shown that the test statistic can be viewed asymptotically as a multiplicative contrast between two estimates of the information matrix, both of which are consistent under correct model specification. This approximation is used to show that the statistic is asymptotically normally distributed, although it is suggested that p values be computed using the parametric bootstrap. The resulting methodology is demonstrated with various examples and simulations involving both discrete and continuous data.

UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno

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