文献详情
Model Confidence Bounds for Variable Selection
文献类型：期刊
期刊名称：Biometrics影响因子和分区
年：2019
ISSN：1541-0420
关键词：Confidence set,Model selection,Uncertainty
摘要：In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identities two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCB proposes a group of nested models as candidat ...More
In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identities two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCB proposes a group of nested models as candidates and the MCB's width and composition enable the practitioner to assess the overall model selection uncertainty. A new graphical tool - the model uncertainty curve (MUC) - is introduced to visualize the variability of model selection and to compare different model selection procedures. The MCB methodology is implemented by a fast bootstrap algorithm that is shown to yield the correct asymptotic coverage under rather general conditions. Our Monte Carlo simulations and real data examples confirm the validity and illustrate the advantages of the proposed method.This article is protected by copyright. All rights reserved. ...Hide

DOI：10.1111/biom.13024
百度学术：Model Confidence Bounds for Variable Selection
语言：外文
人气指数：5
浏览次数：5
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