Posts Tagged With: Jim Berger

U-Phil: Deconstructions [of J. Berger]: Irony & Bad Faith 3

Memory Lane: 2 years ago:
My efficient Errorstat Blogpeople1 have put forward the following 3 reader-contributed interpretive efforts2 as a result of the “deconstruction” exercise from December 11, (mine, from the earlier blog, is at the end) of what I consider:

“….an especially intriguing remark by Jim Berger that I think bears upon the current mindset (Jim is aware of my efforts):

Too often I see people pretending to be subjectivists, and then using “weakly informative” priors that the objective Bayesian community knows are terrible and will give ridiculous answers; subjectivism is then being used as a shield to hide ignorance. . . . In my own more provocative moments, I claim that the only true subjectivists are the objective Bayesians, because they refuse to use subjectivism as a shield against criticism of sloppy pseudo-Bayesian practice. (Berger 2006, 463)” (From blogpost, Dec. 11, 2011)
_________________________________________________
Andrew Gelman:

The statistics literature is big enough that I assume there really is some bad stuff out there that Berger is reacting to, but I think that when he’s talking about weakly informative priors, Berger is not referring to the work in this area that I like, as I think of weakly informative priors as specifically being designed to give answers that are _not_ “ridiculous.”

Keeping things unridiculous is what regularization’s all about, and one challenge of regularization (as compared to pure subjective priors) is that the answer to the question, What is a good regularizing prior?, will depend on the likelihood.  There’s a lot of interesting theory and practice relating to weakly informative priors for regularization, a lot out there that goes beyond the idea of noninformativity.

To put it another way:  We all know that there’s no such thing as a purely noninformative prior:  any model conveys some information.  But, more and more, I’m coming across applied problems where I wouldn’t want to be noninformative even if I could, problems where some weak prior information regularizes my inferences and keeps them sane and under control. Continue reading

Categories: Gelman, Irony and Bad Faith, J. Berger, Statistics, U-Phil | Tags: , , , | 3 Comments

Irony and Bad Faith: Deconstructing Bayesians-reblog

 The recent post by Normal Deviate, and my comments on it, remind me of why/how I got back into the Bayesian-frequentist debates in 2006, as described in my first “deconstruction” (and “U-Phil”) on this blog (Dec 11, 2012):

Some time in 2006 (shortly after my ERROR06 conference), the trickle of irony and sometime flood of family feuds issuing from Bayesian forums drew me back into the Bayesian-frequentist debates.1 2  Suddenly sparks were flying, mostly kept shrouded within Bayesian walls, but nothing can long be kept secret even there. Spontaneous combustion is looming. The true-blue subjectivists were accusing the increasingly popular “objective” and “reference” Bayesians of practicing in bad faith; the new O-Bayesians (and frequentist-Bayesian unificationists) were taking pains to show they were not subjective; and some were calling the new Bayesian kids on the block “pseudo Bayesian.” Then there were the Bayesians somewhere in the middle (or perhaps out in left field) who, though they still use the Bayesian umbrella, were flatly denying the very idea that Bayesian updating fits anything they actually do in statistics.3 Obeisance to Bayesian reasoning remained, but on some kind of a priori philosophical grounds. Doesn’t the methodology used in practice really need a philosophy of its own? I say it does, and I want to provide this. Continue reading

Categories: Likelihood Principle, objective Bayesians, Statistics | Tags: , , , , | 33 Comments

Failing to Apply vs Violating the Likelihood Principle

In writing a new chapter on the Strong Likelihood Principle [i] the past few weeks, I noticed a passage in G. Casella and R. Berger (2002) that in turn recalled a puzzling remark noted in my Jan. 3, 2012 post. The post began:

A question arose from a Bayesian acquaintance:

“Although the Birnbaum result is of primary importance for sampling theorists, I’m still interested in it because many Bayesian statisticians think that model checking violates the (strong) likelihood principle (SLP), as if this principle is a fundamental axiom of Bayesian statistics”.

But this is puzzling for two reasons. First, if the LP does not preclude testing for assumptions (and he is right that it does not[ii]), then why not simply explain that rather than appeal to a disproof of something that actually never precluded model testing?   To take the disproof of the LP as grounds to announce: “So there! Now even Bayesians are free to test their models” would seem only to ingrain the original fallacy.

You can read the rest of the original post here.

The remark in G. Casella and R. Berger seems to me equivocal on this point: Continue reading

Categories: Likelihood Principle, Philosophy of Statistics, Statistics | Tags: , , , | 2 Comments

P-values as Frequentist Measures

Working on the last two chapters of my book on philosophy of statistical inference, I’m revisiting such topics as weak conditioning, Birnbaum, likelihood principle, etc., and was reading from the Proceedings of the Berkeley Conference in Honor of Jerzy Neyman and Jack Kiefer (1985)[i]. In a paper I had not seen (or had forgotten), Jim Berger “The Frequentist Viewpoint and Conditioning,” writes that the quoting of a P-value “may be felt to be a frequentist procedure by some, since it involves an averaging over the sample space. The reporting of P-values can be given no long-run frequency interpretation [in any of the set-ups generally considered].  A P-value actually lies closer to conditional (Bayesian) measures than to frequentist measures.” (Berger 1985, 23). These views are echoed in Berger’s more recent “Could Fisher,Jeffreys and Neyman Have Agreed on Testing?”(2003). This is at odds with what Fisher, N-P, Cox, Lehmann, etc. have held, and if true, would also seem to entail that a severity assessment had no frequentist interpretation!  The flaw lies in that all-too-common behavioristic, predesignated conception…

Among related posts:

https://errorstatistics.com/2012/04/28/3671/
https://errorstatistics.com/2012/05/10/excerpts-from-s-senns-letter-on-replication-p-values-and-evidence/

 


[i] Also because of Peter Gruenwald’s recent mention of Kiefer’s work, read long ago.

Categories: Statistics | Tags: , , , | Leave a comment

Mayo Philosophizes on Stephen Senn: "How Can We Cultivate Senn’s-Ability?"

Where’s Mayo?

Although, in one sense, Senn’s remarks echo the passage of Jim Berger’s that we deconstructed a few weeks ago, Senn at the same time seems to reach an opposite conclusion. He points out how, in practice, people who claim to have carried out a (subjective) Bayesian analysis have actually done something very different—but that then they heap credit on the Bayesian ideal. (See also the blog post “Who Is Doing the Work?”) Continue reading

Categories: Philosophy of Statistics, Statistics, U-Phil | Tags: , , , , | 7 Comments

Model Validation and the LP-(Long Playing Vinyl Record)

A Bayesian acquaintance writes:

Although the Birnbaum result is of primary importance for sampling theorists, I’m still interested in it because many Bayesian statisticians think that model checking violates the likelihood principle, as if this principle is a fundamental axiom of Bayesian statistics.

But this is puzzling for two reasons. First, if the LP does not preclude testing for assumptions (and he is right that it does not[i]), then why not simply explain that rather than appeal to a disproof of something that actually never precluded model testing?   To take the disproof of the LP as grounds to announce: “So there! Now even Bayesians are free to test their models” would seem only to ingrain the original fallacy. Continue reading

Categories: Statistics | Tags: , , , | Leave a comment

JIM BERGER ON JIM BERGER!

Fortunately, we have Jim Berger interpreting himself this evening (see December 11)

Jim Berger writes: 

A few comments:

1. Objective Bayesian priors are often improper (i.e., have infinite total mass), but this is not a problem when they are developed correctly. But not every improper prior is satisfactory. For instance, the constant prior is known to be unsatisfactory in many situations. The ‘solution’ pseudo-Bayesians often use is to choose a constant prior over a large but bounded set (a ‘weakly informative’ prior), saying it is now proper and so all is well. This is not true; if the constant prior on the whole parameter space is bad, so will be the constant prior over the bounded set. The problem is, in part, that some people confuse proper priors with subjective priors and, having learned that true subjective priors are fine, incorrectly presume that weakly informative proper priors are fine. Continue reading

Categories: Irony and Bad Faith, Statistics, U-Phil | Tags: , , , | 13 Comments

Contributed Deconstructions: Irony & Bad Faith 3

My efficient Errorstat Blogpeople1 have put forward the following 3 reader-contributed interpretive efforts2 as a result of the “deconstruction” exercise from December 11, (mine, from the earlier blog, is at the end) of what I consider:

“….an especially intriguing remark by Jim Berger that I think bears upon the current mindset (Jim is aware of my efforts):

Too often I see people pretending to be subjectivists, and then using “weakly informative” priors that the objective Bayesian community knows are terrible and will give ridiculous answers; subjectivism is then being used as a shield to hide ignorance. . . . In my own more provocative moments, I claim that the only true subjectivists are the objective Bayesians, because they refuse to use subjectivism as a shield against criticism of sloppy pseudo-Bayesian practice. (Berger 2006, 463)” (From blogpost, Dec. 11, 2011) Continue reading

Categories: Irony and Bad Faith, Statistics, U-Phil | Tags: , , , | 11 Comments

Irony and Bad Faith: Deconstructing Bayesians 1

Some time in 2006 (shortly after my ERROR06 conference), the trickle of irony and sometime flood of family feuds issuing from Bayesian forums drew me back into the Bayesian-frequentist debates.1 2  Suddenly sparks were flying, mostly kept shrouded within Bayesian walls, but nothing can long be kept secret even there. Spontaneous combustion is looming. The true-blue subjectivists were accusing the increasingly popular “objective” and “reference” Bayesians of practicing in bad faith; the new O-Bayesians (and frequentist-Bayesian unificationists) were taking pains to show they were not subjective; and some were calling the new Bayesian kids on the block “pseudo Bayesian.” Then there were the Bayesians somewhere in the middle (or perhaps out in left field) who, though they still use the Bayesian umbrella, were flatly denying the very idea that Bayesian updating fits anything they actually do in statistics.3 Obeisance to Bayesian reasoning remained, but on some kind of a priori philosophical grounds. Doesn’t the methodology used in practice really need a philosophy of its own? I say it does, and I want to provide this. Continue reading

Categories: Irony and Bad Faith, U-Phil | Tags: , , , , | 6 Comments

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