Consistent information criteria for regularized regression and loss-based learning problems Article Swipe
Qingyuan Zhang
,
Hien D. Nguyen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.17181
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2404.17181
Many problems in statistics and machine learning can be formulated as model selection problems, where the goal is to choose an optimal parsimonious model among a set of candidate models. It is typical to conduct model selection by penalizing the objective function via information criteria (IC), as with the pioneering work by Akaike and Schwarz. Via recent work, we propose a generalized IC framework to consistently estimate general loss-based learning problems. In this work, we propose a consistent estimation method for Generalized Linear Model (GLM) regressions by utilizing the recent IC developments. We advance the generalized IC framework by proposing model selection problems, where the model set consists of a potentially uncountable set of models. In addition to theoretical expositions, our proposal introduces a computational procedure for the implementation of our methods in the finite sample setting, which we demonstrate via an extensive simulation study.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.17181
- https://arxiv.org/pdf/2404.17181
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396243647
All OpenAlex metadata
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https://openalex.org/W4396243647Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.17181Digital Object Identifier
- Title
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Consistent information criteria for regularized regression and loss-based learning problemsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-26Full publication date if available
- Authors
-
Qingyuan Zhang, Hien D. NguyenList of authors in order
- Landing page
-
https://arxiv.org/abs/2404.17181Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2404.17181Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2404.17181Direct OA link when available
- Concepts
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Regression, Regression analysis, Computer science, Information loss, Artificial intelligence, Econometrics, Machine learning, Statistics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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