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