andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-449 knowledge-graph by maker-knowledge-mining
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Introduction: Xuequn Hu writes: I am an econ doctoral student, trying to do some empirical work using Bayesian methods. Recently I read a paper(and its discussion) that pitches Bayesian methods against GMM (Generalized Method of Moments), which is quite popular in econometrics for frequentists. I am wondering if you can, here or on your blog, give some insights about these two methods, from the perspective of a Bayesian statistician. I know GMM does not conform to likelihood principle, but Bayesian are often charged with strong distribution assumptions. I can’t actually help on this, since I don’t know what GMM is. My guess is that, like other methods that don’t explicitly use prior estimation, this method will work well if sufficient information is included as data. Which would imply a hierarchical structure.
sentIndex sentText sentNum sentScore
1 Xuequn Hu writes: I am an econ doctoral student, trying to do some empirical work using Bayesian methods. [sent-1, score-0.558]
2 Recently I read a paper(and its discussion) that pitches Bayesian methods against GMM (Generalized Method of Moments), which is quite popular in econometrics for frequentists. [sent-2, score-0.675]
3 I am wondering if you can, here or on your blog, give some insights about these two methods, from the perspective of a Bayesian statistician. [sent-3, score-0.351]
4 I know GMM does not conform to likelihood principle, but Bayesian are often charged with strong distribution assumptions. [sent-4, score-0.707]
5 I can’t actually help on this, since I don’t know what GMM is. [sent-5, score-0.236]
6 My guess is that, like other methods that don’t explicitly use prior estimation, this method will work well if sufficient information is included as data. [sent-6, score-1.004]
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Introduction: Xuequn Hu writes: I am an econ doctoral student, trying to do some empirical work using Bayesian methods. Recently I read a paper(and its discussion) that pitches Bayesian methods against GMM (Generalized Method of Moments), which is quite popular in econometrics for frequentists. I am wondering if you can, here or on your blog, give some insights about these two methods, from the perspective of a Bayesian statistician. I know GMM does not conform to likelihood principle, but Bayesian are often charged with strong distribution assumptions. I can’t actually help on this, since I don’t know what GMM is. My guess is that, like other methods that don’t explicitly use prior estimation, this method will work well if sufficient information is included as data. Which would imply a hierarchical structure.
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Introduction: Bayesian inference, conditional on the model and data, conforms to the likelihood principle. But there is more to Bayesian methods than Bayesian inference. See chapters 6 and 7 of Bayesian Data Analysis for much discussion of this point. It saddens me to see that people are still confused on this issue.
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Introduction: After reading all the comments here I remembered that I’ve actually written a paper on the generalized method of moments–including the bit about maximum likelihood being a special case. The basic idea is simple enough that it must have been rediscovered dozens of times by different people (sort of like the trapezoidal rule ). In our case, we were motivated to (independently) develop the (well-known, but not by me) generalized method of moments as a way of specifying an indirectly-parameterized prior distribution, rather than as a way of estimating parameters from direct data. But the math is the same.
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Introduction: Robert Bell pointed me to this post by Brad De Long on Bayesian statistics, and then I also noticed this from Noah Smith, who wrote: My impression is that although the Bayesian/Frequentist debate is interesting and intellectually fun, there’s really not much “there” there… despite being so-hip-right-now, Bayesian is not the Statistical Jesus. I’m happy to see the discussion going in this direction. Twenty-five years ago or so, when I got into this biz, there were some serious anti-Bayesian attitudes floating around in mainstream statistics. Discussions in the journals sometimes devolved into debates of the form, “Bayesians: knaves or fools?”. You’d get all sorts of free-floating skepticism about any prior distribution at all, even while people were accepting without question (and doing theory on) logistic regressions, proportional hazards models, and all sorts of strong strong models. (In the subfield of survey sampling, various prominent researchers would refuse to mode
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Introduction: A neuroeconomist asks:: Is there any literature on the Bayesian approach to simultaneous equation systems that you could suggest? (Think demand/supply in econ). My reply: I’m not up-to-date on the Bayesian econometrics literature. TTony Lancaster came out with a book a few years ago that might have some of these models. Maybe you, the commenters, have some suggestions? Measurement-error models are inherently Bayesian, seeing as they have all these latent parameters, so it seems like there should be a lot out there.
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Introduction: Xuequn Hu writes: I am an econ doctoral student, trying to do some empirical work using Bayesian methods. Recently I read a paper(and its discussion) that pitches Bayesian methods against GMM (Generalized Method of Moments), which is quite popular in econometrics for frequentists. I am wondering if you can, here or on your blog, give some insights about these two methods, from the perspective of a Bayesian statistician. I know GMM does not conform to likelihood principle, but Bayesian are often charged with strong distribution assumptions. I can’t actually help on this, since I don’t know what GMM is. My guess is that, like other methods that don’t explicitly use prior estimation, this method will work well if sufficient information is included as data. Which would imply a hierarchical structure.
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Introduction: Joel Greenhouse writes: I saw your recent paper on Feller [see here and, for a more fanciful theory, here ]. Looks like it was fun to write. I recently wrote a paper that asks an orthogonal question to yours. Why during the 1950-1960′s did Jerry Cornfield become a Bayesian? It appeared in Statistics in Medicine – “On becoming a Bayesian: Early correspondences between J. Cornfield and L. J. Savage.” In his paper, Greenhouse writes: Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. . . . Cornfield’s interest in Bayesian methods began prior to 1961 and that the clarity of his Bayesian outlook began to take shape following Birnbaum’s ASA paper on the likelihood prin- cip
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Introduction: From 2006 : Eric Archer forwarded this document by Nick Freemantle, “The Reverend Bayes—was he really a prophet?”, in the Journal of the Royal Society of Medicine: Does [Bayes's] contribution merit the enthusiasms of his followers? Or is his legacy overhyped? . . . First, Bayesians appear to have an absolute right to disapprove of any conventional approach in statistics without offering a workable alternative—for example, a colleague recently stated at a meeting that ‘. . . it is OK to have multiple comparisons because Bayesians’ don’t believe in alpha spending’. . . . Second, Bayesians appear to build an army of straw men—everything it seems is different and better from a Bayesian perspective, although many of the concepts seem remarkably familiar. For example, a very well known Bayesian statistician recently surprised the audience with his discovery of the P value as a useful Bayesian statistic at a meeting in Birmingham. Third, Bayesians possess enormous enthusiasm fo
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Introduction: Antti Rasinen writes: I’m a former undergrad machine learning student and a current software engineer with a Bayesian hobby. Today my two worlds collided. I ask for some enlightenment. On your blog you’ve repeatedly advocated continuous distributions with Bayesian models. Today I read this article by Ricky Ho, who writes: The strength of Bayesian network is it is highly scalable and can learn incrementally because all we do is to count the observed variables and update the probability distribution table. Similar to Neural Network, Bayesian network expects all data to be binary, categorical variable will need to be transformed into multiple binary variable as described above. Numeric variable is generally not a good fit for Bayesian network. The last sentence seems to be at odds with what you’ve said. Sadly, I don’t have enough expertise to say which view of the world is correct. During my undergrad years our team wrote an implementation of the Junction Tree algorithm. We r
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Introduction: Deborah Mayo recommended that I consider coming up with a new name for the statistical methods that I used, given that the term “Bayesian” has all sorts of associations that I dislike (as discussed, for example, in section 1 of this article ). I replied that I agree on Bayesian, I never liked the term and always wanted something better, but I couldn’t think of any convenient alternative. Also, I was finding that Bayesians (even the Bayesians I disagreed with) were reading my research articles, while non-Bayesians were simply ignoring them. So I thought it was best to identify with, and communicate with, those people who were willing to engage with me. More formally, I’m happy defining “Bayesian” as “using inference from the posterior distribution, p(theta|y)”. This says nothing about where the probability distributions come from (thus, no requirement to be “subjective” or “objective”) and it says nothing about the models (thus, no requirement to use the discrete models that hav
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1 0.97321546 999 andrew gelman stats-2011-11-09-I was at a meeting a couple months ago . . .
Introduction: . . . and I decided to amuse myself by writing down all the management-speak words I heard: “grappling” “early prototypes” “technology platform” “building block” “machine learning” “your team” “workspace” “tagging” “data exhaust” “monitoring a particular population” “collective intelligence” “communities of practice” “hackathon” “human resources . . . technologies” Any one or two or three of these phrases might be fine, but put them all together and what you have is a festival of jargon. A hackathon, indeed.
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Introduction: Ring Lardner, Jr.: [In 1936] I was already settled in Southern California, and it may have been that first exercise of the franchise that triggered the FBI surveillance of me that would last for decades. I had assumed, of course, that I was enjoying the vaunted American privilege of the secret ballot. On a wall outside my polling place on Wilshire Boulevard, however, was a compilation of the district’s registered voters: Democrats, a long list of names; Republicans, a somewhat lesser number; and “Declines to State,” one, “Ring W. Lardner, Jr.” The day after the election, alongside those lists were published the results: Roosevelt, so many; Landon, so many; Browder, one.
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Introduction: Kaiser nails it . The offending article , by John Tierney, somehow ended up in the Science section rather than the Opinion section. As an opinion piece (or, for that matter, a blog), Tierney’s article would be nothing special. But I agree with Kaiser that it doesn’t work as a newspaper article. As Kaiser notes, this story involves a bunch of statistical and empirical claims that are not well resolved by P.R. and rhetoric.
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Introduction: A couple months ago, in discussing Charles Murray’s argument that America’s social leaders should “preach what they practice” (Murray argues that they—we!—tend to lead good lives of hard work and moderation but are all too tolerant of antisocial and unproductive behavior among the lower classes), I wrote : Murray does not consider the case of Joe Paterno, but in many ways the Penn State football coach fits his story well. Paterno was said to live an exemplary personal and professional life, combining traditional morality with football success—but, by his actions, he showed little concern about the morality of his players and coaches. At a professional level, Paterno rose higher and higher, and in his personal life he was a responsible adult. But he had an increasing disconnect with the real world, to the extent that horrible crimes were occurring nearby (in the physical and social senses) but he was completely insulated from the consequences for many years. Paterno’s story is s
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Introduction: Arthur Breitman writes: I had to forward this to you when I read about it… My reply: Interesting; thanks. Things like this make me feel so computer-incompetent! The younger generation is passing me by…
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