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Parametric bootstrap inference for stratified models with high-dimensional nuisance specifications
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Bellio, Ruggero, Kosmidis, Ioannis, Salvan, Alessandra and Sartori, Nicola (2023) Parametric bootstrap inference for stratified models with high-dimensional nuisance specifications. Statistica Sinica, 33 (3). pp. 1069-1091. doi:10.5705/ss.202021.0027 ISSN 1017-0405.
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Official URL: https://doi.org/10.5705/ss.202021.0027
Abstract
Inference about a scalar parameter of interest typically relies on the
asymptotic normality of common likelihood pivots, such as the signed likelihood root, the score and Wald statistics. Nevertheless, the resulting inferential procedures are known to perform poorly when the dimension of the nuisance parameter is large relative to the sample size and when the information about the parameters is limited. In many such cases, the use of asymptotic normality of analytical modifications of the signed likelihood root is known to recover inferential performance. It is proved here that parametric bootstrap of standard likelihood pivots results in as accurate inferences as analytical modifications of the signed likelihood root do in stratified models with stratum specific nuisance parameters.
We focus on the challenging case where the number of strata increases as fast or faster than the stratum samples size. It is also shown that this equivalence holds regardless of whether constrained or unconstrained bootstrap is used.
Item Type: | Journal Article | ||||||||||||
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Subjects: | H Social Sciences > HA Statistics Q Science > Q Science (General) Q Science > QA Mathematics |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||||||
Library of Congress Subject Headings (LCSH): | Inference, Estimation theory, Bootstrap (Statistics), Stochastic analysis, Nonparametric statistics, Nonparametric statistics -- Asymptotic theory | ||||||||||||
Journal or Publication Title: | Statistica Sinica | ||||||||||||
Publisher: | Academia Sinica * Institute of Statistical Science | ||||||||||||
ISSN: | 1017-0405 | ||||||||||||
Official Date: | July 2023 | ||||||||||||
Dates: |
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Volume: | 33 | ||||||||||||
Number: | 3 | ||||||||||||
Page Range: | pp. 1069-1091 | ||||||||||||
DOI: | 10.5705/ss.202021.0027 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Description: | OA online not under license |
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Date of first compliant deposit: | 7 January 2022 | ||||||||||||
Date of first compliant Open Access: | 10 January 2022 | ||||||||||||
RIOXX Funder/Project Grant: |
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