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Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Genetic Studies Missing Information Hypothesis Testing
2011/3/23
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Comment: Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies
Quantifying Genetic Studies Missing Information Hypothesis Testing
2011/3/23
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Comment: Quantifying Information Loss in Survival Studies
Comment Survival Quantifying Information Loss in Survival
2011/3/23
Comment on "Quantifying the Fraction of Missing Information for Hypothesis Testing in Statistical and Genetic Studies" [arXiv:1102.2774]
Indirect evidence is crucial for successful statistical practice. Sometimes, however, it is better used informally. Future efforts should be directed toward understanding better the connection between...
It is an honor to comment on Prof. Efron’s latest contribution to the merging of frequentist and Bayesian thinking into a harmonious (even if not strictly coherent) statistical viewpoint.
Comment on “Fastest learning in small-world neural networks”
Feed-forward neural network small-world network random network
2010/3/11
This comment reexamines Simard et al.’s work in [D. Simard, L. Nadeau, H. Kröger, Phys.
Lett. A 336 (2005) 8-15]. We found that Simard et al. calculated mistakenly the local connectivity
length...
Comment on “Harold Jeffreys's Theory of Probability Revisited”
Harold Jeffreys's Theory Probability Revisited
2010/3/9
The authors provide an authoritative lecture guide
of Theory of Probability, where they clearly state
that the more useful material today is that con-
tained in Chapters 3 and 5, which respectively...
Theory of Probability is distinguished by several high-level
philosophical attitudes, some stressed by Jeffreys, some implicit. By
reviewing these we may recognize the importance in this work in the...
Comment on “Harold Jeffreys's Theory of Probability Revisited”
Comment Harold Jeffreys's Theory Probability Revisited
2010/3/9
I have always felt very guilty about Harold Jef-
freys’s Theory of Probability (referred to as ToP,
hereafter). I take seriously George Barnard’s injunc-
tion (Barnard, 1996) to have some familiari...
Comment on “Harold Jeffreys's Theory of Probability Revisited”
Comment Harold Jeffreys's Theory Probability Revisited
2010/3/9
The authors are to be congratulated for their deep
appreciation of Jeffreys’s famous book, Theory of
Probability, and their very impressive, knowledge-
able consideration of its contents, chapter b...
Comment on “Harold Jeffreys's Theory of Probability Revisited”
Comment Harold Jeffreys's Theory Probability Revisited
2010/3/9
I was taught by Harold Jeffreys, having attended
his postgraduate lectures at Cambridge in the aca-
demic year 1946–1947, and also knew him when I
joined the Faculty there. I thought I appreciated ...
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.