Topic
Robust Bayesian analysis
About: Robust Bayesian analysis is a research topic. Over the lifetime, 135 publications have been published within this topic receiving 19439 citations.
Papers published on a yearly basis
Papers
Book•
22 Dec 2012
TL;DR: An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.
Abstract: 1. Basic concepts 2. Utility and loss 3. Prior information and subjective probability 4. Bayesian analysis 5. Minimax analysis 6. Invariance 7. Preposterior and sequential analysis 8. Complete and essentially complete classes Appendices.
8,423 citations
6,641 citations
Book•
1 Jun 1973
TL;DR: In this article, the effect of non-normality on inference about a population mean with generalizations was investigated. But the authors focused on the effect on the mean with information from more than one source.
Abstract: Nature of Bayesian Inference Standard Normal Theory Inference Problems Bayesian Assessment of Assumptions: Effect of Non-Normality on Inferences About a Population Mean with Generalizations Bayesian Assessment of Assumptions: Comparison of Variances Random Effect Models Analysis of Cross Classification Designs Inference About Means with Information from More than One Source: One-Way Classification and Block Designs Some Aspects of Multivariate Analysis Estimation of Common Regression Coefficients Transformation of Data Tables References Indexes.
4,031 citations
Purdue University1, University of Granada2, Simón Bolívar University3, University of Valencia4, University of Murcia5, Autonomous University of Madrid6, Technical University of Madrid7, University of Nottingham8, University of Basel9, University of Rouen10, University College London11, Sapienza University of Rome12, University of Cincinnati13
TL;DR: An overview of the subject of robust Bayesian analysis is provided, one that is accessible to statisticians outside the field, and recent developments in the area are reviewed.
Abstract: Robust Bayesian analysis is the study of the sensitivity of Bayesian answers to uncertain inputs. This paper seeks to provide an overview of the subject, one that is accessible to statisticians outside the field. Recent developments in the area are also reviewed, though with very uneven emphasis.
666 citations
TL;DR: For the one-sided hypothesis testing problem, it is shown in this article that the infimum of the Bayesian posterior probability of H 0 is equal to the p value, while for some classes of prior distributions the infum is less than or equal to p value.
Abstract: For the one-sided hypothesis testing problem it is shown that it is possible to reconcile Bayesian evidence against H 0, expressed in terms of the posterior probability that H 0 is true, with frequentist evidence against H 0, expressed in terms of the p value. In fact, for many classes of prior distributions it is shown that the infimum of the Bayesian posterior probability of H 0 is equal to the p value; in other cases the infimum is less than the p value. The results are in contrast to recent work of Berger and Sellke (1987) in the two-sided (point null) case, where it was found that the p value is much smaller than the Bayesian infimum. Some comments on the point null problem are also given.
412 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 5 |
| 2020 | 2 |
| 2019 | 7 |
| 2018 | 4 |
| 2017 | 3 |
| 2016 | 6 |