Robust Bayesian analysis: sensitivity to the prior

Journal of Statistical Planning and Inference

Volumn 25, Issue 3, 1990, Pages 303-328

  Berger, James O ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38249019651     PISSN: 03783758     EISSN: None     Source Type: Journal    
DOI: 10.1016/0378-3758(90)90079-A     Document Type: Article

References (88)

1. The robust Bayesian viewpoint
2. Are P-values reasonable measures of accuracy?
3. Bayesian input in Stein estimation and a new minimax empirical Bayes estimator
4. Robust Bayes and empirical Bayes analysis with ε-contaminated priors
5. Testing precise hypotheses
6. Ranges of posterior probabilities for unimodal priors with specified quantiles
7. Testing a point null hypothesis: the irreconcilability of significance levels and evidence
8. Incorporating partial prior information: ranges of posterior probabilities
9. Posterior measures under partial prior information
10. Reconciling Bayesian and frequentist evidence in the one-sided testing problem
11. On the application of symmetric Dirichlet distributions and their mixtures to contingency tables
12. Approximate Bayesian predictive distributions and model selection
13. Lower bounds on Bayes factors for interval null hypotheses
14. Lower bounds on Bayes factors for invariant testing situations
15. Lower bounds on Bayes factors for multinomial and chi-squared tests of fit
16. A subjectivist look at robustness
17. Examples relevant to the robustness of applied inferences
18. The use of partial prior knowledge in Bayesian inference
19. Bayesian inference using intervals of measures
20. Approximate posterior distributions
21. Is the tail area useful as an approximate Bayes factor
22. Bayesian statistical inference for psychological research
23. Analysis of decisions with incomplete knowledge of probabilities
24. The linear Bayes regression estimator under weak prior assumptions
25. Rational decisions
26. On the estimation of small frequencies in contingency tables
27. Foundements d'un théorie positive des choix comportant un risque et critique des postulats et axiomes de l'école Américaine
28. Saddle-point methods for the multinomial distribution
29. The appropriate mathematical tools for describing and measuring uncertainty
30. Significance tests in parallel and in series
31. Could a machine make probability judgements?
32. Could a machine make probability judgements?
33. Consistency in statistical inference and decision
34. Subjective probability as the measure of a nonmeasurable set
35. How rational should a manager be?
36. Theory of Probability
37. A Bayesian significance test for the multinomial distribution
38. What is the use of a distribution?
39. The probabilistic explication of evidence, surprise, causality, explanation, and utility
40. Theories of Probability An Examination of Foundations
41. Explicativity, corroboration, and the relative odds of hypotheses
42. The Bayesian influence, or how to sweep subjectivism under the carpet
43. Some history of the hierarchical Bayesian methodology
44. The contributions of Jeffreys to Bayesian statistics
45. Some logic and history of hypothesis testing
46. The axioms of probability
47. Degrees of belief
48. The robustness of a hierarchical model for multinomials and contingency tables
49. J. Statist. Comput. and Simul.
50. Weight of evidence; A brief survey
51. A flexible Bayesian model for comparing two treatments
52. The Bayes/non-Bayes compromise and the multinomial distribution
53. The robustness and sensitivity of the mixed-Dirichlet Bayesian test for ‘independence’ in contingency tables
54. Linear Bayesian methods
55. Robust analysis of the random model and weighted least squares regression
56. Invariance and robustness of the posterior distribution of characteristics of a finite population, with reference to contingency tables and the sampli
57. The use of Choquet capacities in statistics
58. Sensitivity of decisions to probability estimation errors
59. Stable decision problems
60. The general moment problem; a geometric approach
61. The axioms and algebra of intuitive probability
62. The bases of probability
63. On partial prior information and the property of parametric sufficiency
64. Single-parameter inference based on partial prior information
65. Sets of posterior means with bounded variance prior
66. Learning and decision making when subjective probabilities have subjective domains
67. Ranges of posterior probabilities for quasi-unimodal priors with specified quantiles
68. Sensitivity analysis for general and hierarchical linear regression models
69. Quasi-Bayesian behavior: a more realistic approach to decision making?
70. Robust Bayesian estimation
71. Range of posterior measures for priors with arbitrary contaminations
72. Sensitivity of posterior mean to unimodality preserving contaminations
73. Ranges of posterior measures for priors with unimodal contaminations
74. Consistency in statistical inference and decision
75. Robustness of nonideal decision procedures
76. Approximate probability and expectation of gambles
77. Upper and lower probability inferences for the logistic function
78. Bayes-like decision making with upper and lower probabilities