Learning under singularity: an information criterion improving WBIC and sBIC

Liu, Lirui; Suzuki, Joe

doi:https://doi.org/10.1007/s42081-024-00262-1

Learning under singularity: an information criterion improving WBIC and sBIC

Liu, Lirui; Suzuki, Joe

Japanese Journal of Statistics and Data Science, 2024

アクセス数:136件（2025-07-06 09:18 集計）

固定URL: https://hdl.handle.net/11094/98103

閲覧可能ファイル

ファイル	フォーマット	利用条件	サイズ	閲覧回数	利用開始日	説明	information
JpnJStatDataSci_s42081-024-00262-1	pdf	なし	306 KB	44	2024.09.19

Creative Commons : 表示

論文情報

ファイル出力 EndNote Basic出力 Mendeley出力

タイトル	Learning under singularity: an information criterion improving WBIC and sBIC
著者	著者名 Liu, Lirui
著者	NRID 1000050216397 1000050216397 ORCID 0000-0002-3195-9922 0000-0002-3195-9922 researchmap permalink read0185200 read0185200 Scopus Author ID 7401633246 7401633246 著者名 Suzuki, Joe
キーワード等	Information criterion
	Learning under singularity
	Model selection
	Regularity
	Singular Bayesian information criterion
	Widely applicable Bayes information criterion
内容	The version of record of this article, first published in Japanese Journal of Statistics and Data Science, is available online at Publisher’s website: https://doi.org/10.1007/s42081-024-00262-1.
抄録	We introduce a novel information criterion, termed learning under singularity (LS), designed to enhance the functionality of the widely applicable Bayes information criterion (WBIC) and the singular Bayesian information criterion (sBIC). LS is effective without regularity constraints and demonstrates stability. Watanabe defined a statistical model or a learning machine as regular if the mapping from a parameter to a probability distribution is one-to-one and its Fisher information matrix is positive definite. In contrast, models not meeting these conditions are termed singular. Over the past decade, several information criteria for singular cases have been proposed, including WBIC and sBIC. WBIC is applicable in non-regular scenarios but faces challenges with large sample sizes and redundant estimation of known learning coefficients. Conversely, sBIC is limited in its broader application due to its dependence on maximum likelihood estimates. LS addresses these limitations by enhancing the utility of both WBIC and sBIC. It incorporates the empirical loss from the widely applicable information criterion (WAIC) to represent the goodness of fit to the statistical model, along with a penalty term similar to that of sBIC. This approach offers a flexible and robust method for model selection, free from regularity constraints.
出版者	Springer
収録物名	Japanese Journal of Statistics and Data Science
刊行年月	2024-08-21
言語	英語
ハンドルURL	https://hdl.handle.net/11094/98103
eISSN	25208764
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1007/s42081-024-00262-1
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	Japanese Journal of Statistics and Data Science. 2024
DC.title	Learning under singularity: an information criterion improving WBIC and sBIC
DC.creator	Liu, Lirui
DC.creator	Suzuki, Joe
DC.publisher	Springer
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-08-21
DC.identifier	https://doi.org/10.1007/s42081-024-00262-1
DC.subject	Information criterion
	Learning under singularity
	Model selection
	Regularity
	Singular Bayesian information criterion
	Widely applicable Bayes information criterion
DC.description	The version of record of this article, first published in Japanese Journal of Statistics and Data Science, is available online at Publisher’s website: https://doi.org/10.1007/s42081-024-00262-1.
DCTERMS.abstract	We introduce a novel information criterion, termed learning under singularity (LS), designed to enhance the functionality of the widely applicable Bayes information criterion (WBIC) and the singular Bayesian information criterion (sBIC). LS is effective without regularity constraints and demonstrates stability. Watanabe defined a statistical model or a learning machine as regular if the mapping from a parameter to a probability distribution is one-to-one and its Fisher information matrix is positive definite. In contrast, models not meeting these conditions are termed singular. Over the past decade, several information criteria for singular cases have been proposed, including WBIC and sBIC. WBIC is applicable in non-regular scenarios but faces challenges with large sample sizes and redundant estimation of known learning coefficients. Conversely, sBIC is limited in its broader application due to its dependence on maximum likelihood estimates. LS addresses these limitations by enhancing the utility of both WBIC and sBIC. It incorporates the empirical loss from the widely applicable information criterion (WAIC) to represent the goodness of fit to the statistical model, along with a penalty term similar to that of sBIC. This approach offers a flexible and robust method for model selection, free from regularity constraints.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	98103
citation_title	Learning under singularity: an information criterion improving WBIC and sBIC
citation_author	Liu, Lirui
citation_author	Suzuki, Joe
citation_publisher	Springer
citation_language	英語
citation_date	2024-08-21
citation_journal_title	Japanese Journal of Statistics and Data Science
citation_doi	https://doi.org/10.1007/s42081-024-00262-1
citation_public_url	https://hdl.handle.net/11094/98103

Learning under singularity: an information criterion improving WBIC and sBIC

閲覧可能ファイル

論文情報

論文詳細を表示