Semiparametric empirical likelihood inference with estimating equations under density ratio models Article Swipe
Meng Yuan
,
Pengfei Li
,
Changbao Wu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1214/22-ejs2069
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1214/22-ejs2069
The density ratio model (DRM) provides a flexible and useful platform for combining information from multiple sources. In this paper, we consider statistical inference under two-sample DRMs with additional parameters defined through and/or additional auxiliary information expressed as estimating equations. We examine the asymptotic properties of the maximum empirical likelihood estimators (MELEs) of the unknown parameters in the DRMs and/or defined through estimating equations, and establish the chi-square limiting distributions for the empirical likelihood ratio (ELR) statistics. We show that the asymptotic variance of the MELEs of the unknown parameters does not decrease if one estimating equation is dropped. Similar properties are obtained for inferences on the cumulative distribution function and quantiles of each of the populations involved. We also propose an ELR test for the validity and usefulness of the auxiliary information. Simulation studies show that correctly specified estimating equations for the auxiliary information result in more efficient estimators and shorter confidence intervals. Two real examples are used for illustrations.
Related Topics
Concepts
Mathematics
Empirical likelihood
Estimator
Quantile
Estimating equations
Statistics
Asymptotic distribution
Likelihood function
Inference
Applied mathematics
Likelihood-ratio test
Fisher information
Statistical inference
Confidence interval
Delta method
Generalized estimating equation
Ratio estimator
Estimation theory
Efficient estimator
Computer science
Artificial intelligence
Minimum-variance unbiased estimator
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1214/22-ejs2069
- https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-2/Semiparametric-empirical-likelihood-inference-with-estimating-equations-under-density-ratio/10.1214/22-EJS2069.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312834984
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4312834984Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1214/22-ejs2069Digital Object Identifier
- Title
-
Semiparametric empirical likelihood inference with estimating equations under density ratio modelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Meng Yuan, Pengfei Li, Changbao WuList of authors in order
- Landing page
-
https://doi.org/10.1214/22-ejs2069Publisher landing page
- PDF URL
-
https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-2/Semiparametric-empirical-likelihood-inference-with-estimating-equations-under-density-ratio/10.1214/22-EJS2069.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-2/Semiparametric-empirical-likelihood-inference-with-estimating-equations-under-density-ratio/10.1214/22-EJS2069.pdfDirect OA link when available
- Concepts
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Mathematics, Empirical likelihood, Estimator, Quantile, Estimating equations, Statistics, Asymptotic distribution, Likelihood function, Inference, Applied mathematics, Likelihood-ratio test, Fisher information, Statistical inference, Confidence interval, Delta method, Generalized estimating equation, Ratio estimator, Estimation theory, Efficient estimator, Computer science, Artificial intelligence, Minimum-variance unbiased estimatorTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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