模型选择后的推理引导和似然模型的模型平均法

IF 0.9 4区数学 Q3 STATISTICS & PROBABILITY

Metrika Pub Date : 2024-03-05 DOI:10.1007/s00184-024-00956-2

Andrea C. Garcia-Angulo, Gerda Claeskens

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引用次数: 0

摘要

本文构建了一种一步半参数引导程序，用于估计模型选择后估计子的分布，以及带有数据相关权重的模型平均估计子的分布。该方法一般适用于非正态模型。允许对所有候选参数模型进行错误规范。半参数自举估计器在特定区域内是一致的，因此好的和坏的候选模型是分开的。仿真研究表明，自举程序可以得到覆盖范围较小的置信区间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Bootstrap for inference after model selection and model averaging for likelihood models

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Bootstrap for inference after model selection and model averaging for likelihood models

A one-step semiparametric bootstrap procedure is constructed to estimate the distribution of estimators after model selection and of model averaging estimators with data-dependent weights. The method is generally applicable to non-normal models. Misspecification is allowed for all candidate parametric models. The semiparametric bootstrap estimator is shown to be consistent within specific regions such that the good and the bad candidate models are separated. Simulation studies exemplify that the bootstrap procedure leads to short confidence intervals with a good coverage.

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来源期刊

Metrika 数学-统计学与概率论

CiteScore

1.50

自引率

14.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Metrika is an international journal for theoretical and applied statistics. Metrika publishes original research papers in the field of mathematical statistics and statistical methods. Great importance is attached to new developments in theoretical statistics, statistical modeling and to actual innovative applicability of the proposed statistical methods and results. Topics of interest include, without being limited to, multivariate analysis, high dimensional statistics and nonparametric statistics; categorical data analysis and latent variable models; reliability, lifetime data analysis and statistics in engineering sciences.