Statistical Model Choice

Gerda Claeskens

doi:10.1146/annurev-statistics-041715-033413

2016-06-01

Gerda Claeskens *

DOI：10.1146/annurev-statistics-041715-033413

原文链接

查看原文

摘要

En 中文

Variable selection methods and model selection approaches are valuable statistical tools that are indispensable for almost any statistical modeling question. This review first considers the use of information criteria for model selection. Such criteria provide an ordering of the considered models where the best model is selected. Different modeling goals might require different criteria to be used. Next, the effect of including a penalty in the estimation process is discussed. Third, nonparametric estimation is discussed; it contains several aspects of model choice, such as the choice of the estimator to use and the selection of tuning parameters. Fourth, model averaging approaches are reviewed in which estimators from different models are weighted to provide one final estimator. There are several ways to choose the weights, and most of them result in data-driven, hence random, weights. Challenges for inference after model selection and inference for model-averaged estimators are discussed.

Keyword:

model selection

variable selection

information criteria

model averaging

AI总结

对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Annual Review of Statistics and Its Application

IF:

8.7

论文数:

211

被引数:

2.4K

机构

KU Leuven

学者数:

5.7W

论文数: 5.2W

被引数: 8.1W

引用论文

Managing International Business in Relation-Based versus Rule-Based Countries

IF0

2009-12-09

PREAI

Shaomin Li

Luminescence dynamics and carbon-13 NMR characteristics of dinuclear complexes exhibiting coupled lanthanide(III) cation pairs

The Journal of Physical Chemistry

IF0

2002-05-01

PREAI

Karen D. Matthews; Susan A. Bailey-Folkes; Ishenkumba A. Kahwa; Gary L. McPherson; Caroline A. O'Mahoney; Steven V. Ley; David J. Williams; Christopher J. Groombridge; Charles A. O'Connor