On semi-supervised estimation using exponential tilt mixture models Article Swipe
Ye Tian
,
Xinwei Zhang
,
Zhiqiang Tan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.08504
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.08504
Consider a semi-supervised setting with a labeled dataset of binary responses and predictors and an unlabeled dataset with only the predictors. Logistic regression is equivalent to an exponential tilt model in the labeled population. For semi-supervised estimation, we develop further analysis and understanding of a statistical approach using exponential tilt mixture (ETM) models and maximum nonparametric likelihood estimation, while allowing that the class proportions may differ between the unlabeled and labeled data. We derive asymptotic properties of ETM-based estimation and demonstrate improved efficiency over supervised logistic regression in a random sampling setup and an outcome-stratified sampling setup previously used. Moreover, we reconcile such efficiency improvement with the existing semiparametric efficiency theory when the class proportions in the unlabeled and labeled data are restricted to be the same. We also provide a simulation study to numerically illustrate our theoretical findings.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.08504
- https://arxiv.org/pdf/2311.08504
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388747808
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- OpenAlex ID
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https://openalex.org/W4388747808Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2311.08504Digital Object Identifier
- Title
-
On semi-supervised estimation using exponential tilt mixture modelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-14Full publication date if available
- Authors
-
Ye Tian, Xinwei Zhang, Zhiqiang TanList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.08504Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.08504Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2311.08504Direct OA link when available
- Concepts
-
Logistic regression, Mathematics, Exponential function, Statistics, Nonparametric statistics, Population, Artificial intelligence, Computer science, Pattern recognition (psychology), Applied mathematics, Demography, Mathematical analysis, SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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