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Abstract
In practice often either the Bayesian or frequentist method is used, although there are some combined uses of the two methods, a formal unified methodology of the two hasn’t been seen. Here we first give a brief review of the two methods and some combination of the two, then propose a procedure using both the frequentist likelihood and the Bayesian posterior loss in parameter estimation and hypothesis testing, as an attempt to unify the two methods. Basic properties of the proposed method are studied, and simulation studies are carried out to evaluate the performance of the method.
| Original language | English |
|---|---|
| Pages (from-to) | 9-26 |
| Journal | Statistics and Its Interface |
| Volume | 17 |
| Issue number | 1 |
| Online published | 27 Nov 2023 |
| DOIs | |
| Publication status | Published - 2024 |
Funding
The authors are very grateful to the Co-editors Professor Ming-Hui Chen and Professor Yuedong Wang, an associate editor, and two reviewers for their very helpful and constructive comments. Xu’s work was supported in part by General Research Fund (17308820) of Hong Kong.
Research Keywords
- Bayesian estimate
- Compound inference
- High order behavior
- Maximum likelihood estimate
RGC Funding Information
- RGC-funded
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Dive into the research topics of 'Frequentist Bayesian compound inference'. Together they form a unique fingerprint.Projects
- 1 Finished
-
GRF: Dynamic and Large-scale Network Survival Analysis
XU, J. (Principal Investigator / Project Coordinator)
31/07/20 → 11/07/25
Project: Research
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