Information criteria for model selection
Information criteria for model selection
复制标题
模型选择的信息标准
DOI:
10.1002/wics.1607
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发表时间:
2023
期刊:
影响因子:
--
通讯作者:
Ding, Jie
中科院分区:
文献类型:
--
作者:
Zhang, Jiawei;Yang, Yuhong;Ding, Jie
The rapid development of modeling techniques has brought many opportunities for data‐driven discovery and prediction. However, this also leads to the challenge of selecting the most appropriate model for any particular data task. Information criteria, such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC), have been developed as a general class of model selection methods with profound connections with foundational thoughts in statistics and information theory. Many perspectives and theoretical justifications have been developed to understand when and how to use information criteria, which often depend on particular data circumstances. This review article will revisit information criteria by summarizing their key concepts, evaluation metrics, fundamental properties, interconnections, recent advancements, and common misconceptions to enrich the understanding of model selection in general.This article is categorized under:Data: Types and Structure > Traditional Statistical DataStatistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Information Theoretic MethodsStatistical Models > Model Selection
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DOI:
10.1007/bf01599020
发表时间:
1982
期刊:
Circuits, Systems and Signal Processing
影响因子:
--
作者:
J. Rissanen
通讯作者:
J. Rissanen
影响因子:
6.8
作者:
AKAIKE, H
通讯作者:
AKAIKE, H
DOI:
10.1080/01621459.2021.1979010
发表时间:
2019-11
影响因子:
3.7
作者:
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通讯作者:
Jiawei Zhang;Jie Ding;Yuhong Yang
DOI:
10.1198/016214506000000735
发表时间:
2006-12-01
影响因子:
3.7
作者:
Zou, Hui
通讯作者:
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影响因子:
1.5
作者:
Zhang, Jiawei;Ding, Jie;Yang, Yuhong
通讯作者:
Yang, Yuhong