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A Default Conjugate Prior for Variance Components in Generalized Linear Mixed Models(Comment on Article by Browne and Draper)
Choice of prior hierarchical models noninformative priors random effects
2009/9/21
For a scalar random-eect variance, Browne and Draper (2005) have found that the uniform prior works well. It would be valuable to know more about the vector case, in which a second-stage prior on the ...
Prior distributions for variance parameters in hierarchical models(Comment on Article by Browne and Draper)
Bayesian inference conditional conjugacy folded-noncentral-t distribution hierarchical model multilevel mode weakly informative prior distribution
2009/9/21
Various noninformative prior distributions have been suggested for
scale parameters in hierarchical models. We construct a new folded-noncentral-t
family of conditionally conjugate priors for hierar...
Frequentist Bayes is Objective(Comment on Articles by Berger and by Goldstein)
coverage frequency probability objective Bayesian inference
2009/9/21
In this comment, I argue that Bayes procedures with good frequentist
properties are objective. I introduce the idea with a short play, followed by some
commentary.
Science, Subjectivity and Software(Comment on Articles by Berger and by Goldstein)
Science Subjectivity Software
2009/9/21
The dangerous heresy of so-called ojective Bayesian methods is again propounded by Berger. These comments are my attempt to save Bayesian statistics.
Philosophy is consequential because it guides action. In statistics, our beliefs about
the nature of statistical inference guide us both in selecting statistical methods to apply
and in choosing pro...
Does it Make Sense to be an "Objective Bayesian"?(Comment on Articles by Berger and by Goldstein)
Dutch book Holy Grail Normative theory Subjective likelihood
2009/9/21
The subjective-objective dialogue between Goldstein (2006) and Berger
(2006) lays out strong cases for what seem to be two schools of Bayesian thought.
But a closer look suggests to me that while bo...
Coherence and calibration: comments on subjectivity and "objectivity" in Bayesian analysis(Comment on Articles by Berger and by Goldstein)
Meta-analysis out-of-sample predictive calibration
2009/9/21
In this contribution to the discussion of Th case for objective
Bayesian analysis by James Berger and Sujective Bayesian analysis: princi-
ples and practice by Michael Goldstein, I argue that (a) al...
On the Selection of Irregular，Misspecified Regression Models：a Comment on Folklore
Consistency misspecified models model selection regression
2009/3/5
In this paper we will investigate the consequences of applying model selection methods under regularity conditions that are sufficiently general to encompass (i) stochastic models involving non-statio...
Comment：Gibbs Sampling，Exponential Families and Orthogonal Polynomials
Comment Gibbs Sampling Exponential Families Orthogonal Polynomials
2010/4/30
Let K be a reversible Markov kernel on a measurable
space (S,B) with stationary distribution P.
Regard K as a linear operator, K:L2(P)→L2(P),
and suppose that L2(P) admits an orthonormal basis
of ...
Comment：Lancaster Probabilities and Gibbs Sampling
Comment Lancaster Probabilities Gibbs Sampling
2010/4/30
It is a pleasure to congratulate the authors for
this excellent, original and pedagogical paper. I read
a preliminary draft at the end of 2006 and I then
mentioned to the authors that their work sh...
We congratulate the authors on a review of convergence
rates for Gibbs sampling routines. Their
combined work on studying convergence rates via
orthogonal polynomials in the present paper under
di...
Comment：Microarrays，Empirical Bayes and the Two-Groups Model
Microarrays Empirical Bayes Two-Groups Model
2010/4/30
Efron has given us a comprehensive and thoughtful
review of his approach to large-scale testing stemming
from the challenges of analyzing microarray
data. Addressing the microarray challenge right ...
Comment：The 2005 Neyman Lecture：Dynamic Indeterminism in Science
Comment 2005 Neyman Lecture Dynamic Indeterminism Science
2010/4/30
Professor Brillinger is to be congratulated on this
paper which is both a contribution to the history of
statistics and an introduction to statistical modeling
using stochastic processes, a topic t...