Information criteria for model selection

Information criteria for model selection
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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:
WIREs Computational Statistics
影响因子:
--
通讯作者:
Ding, Jie
Ding, Jie
中科院分区:
--
文献类型:
--
作者:
Zhang, Jiawei;Yang, Yuhong;Ding, Jie

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建模技术的快速发展为数据驱动的发现和预测带来了许多机会。然而,这也带来了为任何特定数据任务选择最合适模型的挑战。信息准则,如赤池信息准则(AIC)和贝叶斯信息准则(BIC),是与统计学和信息论的基本思想有着深刻联系的一类模型选择方法。许多观点和理论的理由已经发展,以了解何时以及如何使用信息标准,这往往取决于特定的数据环境。这篇综述文章将通过总结信息标准的关键概念、评估指标、基本属性、相互关系、最新进展和常见误解来重新审视信息标准,以丰富对模型选择的理解。类型和结构>传统统计数据数据科学的统计学习和探索方法>建模方法数据分析的统计和图形方法>信息论方法统计模型>模型选择
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
通过最小描述长度估计结构
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
DOI: 10.1109/tac.1974.1100705
发表时间: 1974-01-01
影响因子: 6.8
作者:
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DOI: 10.1080/01621459.2021.1979010
发表时间: 2019-11
影响因子: 3.7
作者:
Jiawei Zhang;Jie Ding;Yuhong Yang
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DOI: 10.1198/016214506000000735
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有针对性的交叉验证
DOI: --
发表时间: 2022
期刊: Bernoulli
影响因子: 1.5
作者:
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