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Activity Number: 465 - SPEED: Statistical Computing: Methods, Implementation, and Application, Part 1
Type: Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #305316 Presentation
Title: A Simple Recipe for Making Accurate Parametric Inference in Finite Sample
Author(s): Mucyo Karemera* and Stephane Guerrier and Samuel Orso and Maria-Pia Victoria-Feser
Companies: Penn State University and University of Geneva and University of Geneva and University of Geneva
Keywords: M-estimator; Two-steps estimators; Indirect inference; Bootstrap

Constructing tests or confidence regions that control over the error rates in the long-run is probably one of the most important problem in statistics. Yet, the theoretical justification for most methods in statistics is asymptotic. The bootstrap for example, despite its simplicity and its widespread usage, is an asymptotic method. There are in general no claim about the exactness of inferential procedures in finite sample. In this paper, we propose an alternative to the parametric bootstrap. We setup general conditions to demonstrate theoretically that accurate inference can be claimed in finite sample.

Authors who are presenting talks have a * after their name.

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