andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2254 knowledge-graph by maker-knowledge-mining
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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
sentIndex sentText sentNum sentScore
1 From 2006 : Eric Archer forwarded this document by Nick Freemantle, “The Reverend Bayes—was he really a prophet? [sent-1, score-0.066]
2 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 ‘. [sent-7, score-0.539]
3 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. [sent-14, score-0.193]
4 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. [sent-15, score-0.092]
5 The key phrase is “complex nonlinear mixed models. [sent-22, score-0.465]
6 ” Not too long ago, anti-Bayesians used to say that Bayesian inference was worthless because it only worked on simple linear models. [sent-23, score-0.135]
7 Now their last resort is to say that it only works for complex nonlinear models! [sent-24, score-0.677]
8 I’ll let the non-Bayesians use their methods for linear regression (as long as there aren’t too many predictors; then you need a “complex mixed model”), and the Bayesians can handle everything complex, nonlinear, and mixed. [sent-26, score-0.369]
9 For many simple problems, the Bayesian and classical methods give similar answers. [sent-28, score-0.131]
10 But when things start to get complex and nonlinear, it’s simpler to go Bayesian. [sent-29, score-0.315]
11 (As a minor point: the starting distribution for the Gibbs sampler is not the same as the prior distribution, and also that Freemantle appears to be conflating a computational tool with an approach to inference. [sent-30, score-0.365]
12 Perhaps Bayesians could file their applications for disapproval through some sort of institutional review board? [sent-33, score-0.068]
13 Maybe someone in the medical school could tell us when we’re allowed to disapprove and when we can’t. [sent-34, score-0.283]
14 But, in all seriousness, I do think it’s a step forward that Bayesian methods are associated with “complex nonlinear mixed models. [sent-38, score-0.596]
15 ” That’s not a bad association to have, since I think complex models are more realistic. [sent-39, score-0.391]
16 To go back to the medical context, complex models can allow treatments to have different effects in different subpopulations, and can help control for imbalance in observational studies. [sent-40, score-0.523]
17 Update (2014): There’s something that fascinates me about these aggressive anti-Bayesians: it’s not enough for them to simply restrict their own practice to non-Bayesian methods; they have to go the next step and put down Bayesian methods that they don’t even understand. [sent-41, score-0.199]
18 Just to remove any ambiguity here: I have no problem with non -Bayesians: those statisticians who for whatever combination of theoretical or applied reasons prefer not to use Bayesian methods in their own work. [sent-48, score-0.131]
19 My problem is with anti -Bayesians who denigrate the Bayesian approach from a position of lack of understanding. [sent-49, score-0.072]
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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: In this discussion from last month, computer science student and Judea Pearl collaborator Elias Barenboim expressed an attitude that hierarchical Bayesian methods might be fine in practice but that they lack theory, that Bayesians can’t succeed in toy problems. I posted a P.S. there which might not have been noticed so I will put it here: I now realize that there is some disagreement about what constitutes a “guarantee.” In one of his comments, Barenboim writes, “the assurance we have that the result must hold as long as the assumptions in the model are correct should be regarded as a guarantee.” In that sense, yes, we have guarantees! It is fundamental to Bayesian inference that the result must hold if the assumptions in the model are correct. We have lots of that in Bayesian Data Analysis (particularly in the first four chapters but implicitly elsewhere as well), and this is also covered in the classic books by Lindley, Jaynes, and others. This sort of guarantee is indeed p
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Introduction: I came across this article on the philosophy of statistics by University of Michigan economist John DiNardo. I don’t have much to say about the substance of the article because most of it is an argument against something called “Bayesian methods” that doesn’t have much in common with the Bayesian data analysis that I do. If an quantitative, empirically-minded economist at a top university doesn’t know about modern Bayesian methods, then it’s a pretty good guess that confusion holds in many other quarters as well, so I thought I’d try to clear a couple of things up. (See also here .) In the short term, I know I have some readers at the University of Michigan, so maybe a couple of you could go over to Prof. DiNardo’s office and discuss this with him? For the rest of you, please spread the word. My point here is not to claim that DiNardo should be using Bayesian methods or to claim that he’s doing anything wrong in his applied work. It’s just that he’s fighting against a bu
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Introduction: I’ll answer the above question after first sharing some background and history on the the philosophy of Bayesian statistics, which appeared at the end of our rejoinder to the discussion to which I linked the other day: When we were beginning our statistical educations, the word ‘Bayesian’ conveyed membership in an obscure cult. Statisticians who were outside the charmed circle could ignore the Bayesian subfield, while Bayesians themselves tended to be either apologetic or brazenly defiant. These two extremes manifested themselves in ever more elaborate proposals for non-informative priors, on the one hand, and declarations of the purity of subjective probability, on the other. Much has changed in the past 30 years. ‘Bayesian’ is now often used in casual scientific parlance as a synonym for ‘rational’, the anti-Bayesians have mostly disappeared, and non-Bayesian statisticians feel the need to keep up with developments in Bayesian modelling and computation. Bayesians themselves
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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: 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: 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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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: From 2006 : Naseem Taleb ‘s publisher sent me a copy of “Fooled by randomness: the hidden role of chance in life and the markets” to review. It’s an important topic, and the book is written in a charming style—I’ll try to respond in kind, with some miscellaneous comments. On the cover of the book is a blurb, “Named by Fortune one of the smartest books of all time.” But Taleb instructs us on page 161-162 to ignore book reviews because of selection bias (the mediocre reviews don’t make it to the book cover). Books vs. articles I prefer writing books to writing journal articles because books are written for the reader (and also, in the case of textbooks, for the teacher), whereas articles are written for referees. Taleb definitely seems to be writing to the reader, not the referee. There is risk in book-writing, since in some ways referees are the ideal audience of experts, but I enjoy the freedom in book-writing of being able to say what I really think. Variation and rando
Introduction: Jeff Leek just posted the discussions of his paper (with Leah Jager), “An estimate of the science-wise false discovery rate and application to the top medical literature,” along with some further comments of his own. Here are my original thoughts on an earlier version of their article. Keith O’Rourke and I expanded these thoughts into a formal comment for the journal. We’re pretty much in agreement with John Ioannidis (you can find his discussion in the top link above). In quick summary, I agree with Jager and Leek that this is an important topic. I think there are two key places where Keith and I disagree with them: 1. They take published p-values at face value whereas we consider them as the result of a complicated process of selection. This is something I didn’t used to think much about, but now I’ve become increasingly convinced that the problems with published p-values is not a simple file-drawer effect or the case of a few p=0.051 values nudged toward p=0.049, bu
Introduction: Jed Dougherty points me to this opinion piece by Jacqueline Stevens, a professor of art at Northwestern University, who writes: Artists are defensive these days because in May the House passed an amendment to a bill eliminating the National Endowment for the Arts. Colleagues, especially those who have received N.E.A. grants, will loathe me for saying this, but just this once I’m sympathetic with the anti-intellectual Republicans behind this amendment. Why? The bill incited a national conversation about a subject that has troubled me for decades: the government — disproportionately — supports art that I do not like. Actually, just about nobody likes modern art. All those soup cans—what’s that all about? The stuff they have in museums nowadays, my 4-year-old could do better than that. Two-thirds of so-called modern artists are drunk and two-thirds are frauds. And, no, I didn’t get my math wrong—there’s just a lot of overlap among these categories! It’s an open secret in my
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