Conditional inference in parametric models

Broniatowski, Michel; Caron, Virgile

Journal de la société française de statistique, Tome 160 (2019) no. 2, pp. 48-66.

Cet article propose une nouvelle approche d’inférence statistique, fondée sur la simulation d’échantillons conditionnés par une statistique des données. L’approximation de la vraisemblance conditionnelle de longues séries d’échantillons sachant la statistique des données admet une forme explicite qui est présentée. Lorsque la statistique de conditionnement est exhaustive par rapport à un paramètre fixé, on montre que la densité approchée est également invariante par rapport à ce même paramètre. Une nouvelle procédure de Rao-Blackwell est proposée et les simulations réalisées montrent que le théorème de Lehmann Scheffé reste valide pour cette approximation. L’inférence conditionnelle sur les familles exponentielles avec paramètre de nuisance est également étudiée, menant à des tests de Monte Carlo, dont les performances sur échantillonnage conditionnel sont comparées à celles sur bootstrap paramétrique. Enfin, on s’intéresse à l’estimation du paramètre d’intérêt par la vraisemblance conditionnelle.

This paper presents a new approach to conditional inference, based on the simulation of samples conditioned by a statistics of the data. Also an explicit expression for the approximation of the conditional likelihood of long runs of the sample given the observed statistics is provided. It is shown that when the conditioning statistics is sufficient for a given parameter, the approximating density is still invariant with respect to the parameter. A new Rao-Blackwellisation procedure is proposed and simulation shows that Lehmann Scheffé Theorem is valid for this approximation. Conditional inference for exponential families with nuisance parameter is also studied, leading to Monte Carlo tests; comparison with the parametric bootstrap method is discussed. Finally the estimation of the parameter of interest through conditional likelihood is considered.

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