andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1304 knowledge-graph by maker-knowledge-mining
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Introduction: Shalizi . But this one is still my favorite.
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Introduction: Shalizi . But this one is still my favorite.
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Introduction: My article with Cosma Shalizi has appeared in the British Journal of Mathematical and Statistical Psychology. I’m so glad this paper has come out. I’d been thinking about writing such a paper for almost 20 years. What got me to actually do it was an invitation a few years ago to write a chapter on Bayesian statistics for a volume on the philosophy of social sciences. Once I started doing that, I realized I had enough for a journal article. I contacted Cosma because he, unlike me, was familiar with the post-1970 philosophy literature (my knowledge went only up to Popper, Kuhn, and Lakatos). We submitted it to a couple statistics journals that didn’t want it (for reasons that weren’t always clear ), but ultimately I think it ended up in the right place, as psychologists have been as serious as anyone in thinking about statistical foundations in recent years. Here’s the issue of the journal , which also includes an introduction, several discussions, and a rejoinder: Prior app
3 0.13393572 1988 andrew gelman stats-2013-08-19-BDA3 still (I hope) at 40% off! (and a link to one of my favorite papers)
Introduction: Follow the Amazon link and check to see if it’s still on sale . P.S. I don’t make any money through this link. We do get some royalties from the book, but only a very small amount. I’m pushing the Amazon link right now because (a) I think the book is great, and I want as many people as possible to have it, and (b) 40% off is a pretty good deal and I don’t know how long this will last. P.P.S. Just so this post has some statistical content, here’s one of my favorite papers , Bayesian model-building by pure thought: some principles and examples. It’s from 1996, and here’s the abstract:
4 0.1280794 316 andrew gelman stats-2010-10-03-Suggested reading for a prospective statistician?
Introduction: Sam Jessup writes: I am writing to ask you to recommend papers, books–anything that comes to mind that might give a prospective statistician some sense of what the future holds for statistics (and statisticians). I have a liberal arts background with an emphasis in mathematics. It seems like this is an exciting time to be a statistician, but that’s just from the outside looking in. I’m curious about your perspective on the future of the discipline. Any recommendations? My favorite is still the book, “Statistics: A Guide to the Unknown,” first edition. (I actually have a chapter in the latest (fourth) edition, but I think the first edition (from 1972, I believe) is still the best.
5 0.12352652 1700 andrew gelman stats-2013-01-31-Snotty reviewers
Introduction: I had a submission a couple years ago that was rejected by a journal. One of the reviewers began with the following snotty aside: In this manuscript Gelman and Shalizi (there’s no anonymity here; this thing has been floating around the web for some time) . . . Actually, we posted it on the same day we submitted it to the journal. But double-blindness allowed the reviewer to act as if we had done something wrong! And, even if it had been “floating around the web for some time,” that’s not necessarily a bad thing. Perhaps it just meant that the article had previously been rejected by a bad-attitude reviewer!
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Introduction: Shalizi . But this one is still my favorite.
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Introduction: Philosophie et practique de la statistique bayésienne . I’ll try to update the slides a bit since a few years ago , to add some thoughts I’ve had recently about problems with noninformative priors, even in simple settings. The location of the talk will not be convenient for most of you, but anyone who comes to the trouble of showing up will have the opportunity to laugh at my accent. P.S. For those of you who are interested in the topic but can’t make it to the talk, I recommend these two papers on my non-inductive Bayesian philosophy: [2013] Philosophy and the practice of Bayesian statistics (with discussion). {\em British Journal of Mathematical and Statistical Psychology} {\bf 66}, 8–18. (Andrew Gelman and Cosma Shalizi) [2013] Rejoinder to discussion. (Andrew Gelman and Cosma Shalizi) [2011] Induction and deduction in Bayesian data analysis. {\em Rationality, Markets and Morals}, special topic issue “Statistical Science and Philosophy of Science: Where Do (Should)
3 0.5340212 2157 andrew gelman stats-2014-01-02-2013
Introduction: There’s lots of overlap but I put each paper into only one category. Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later. So you can can think of this list as representing roughly two years’ work. Political science: [2014] The twentieth-century reversal: How did the Republican states switch to the Democrats and vice versa? {\em Statistics and Public Policy}. (Andrew Gelman) [2013] Hierarchical models for estimating state and demographic trends in U.S. death penalty public opinion. {\em Journal of the Royal Statistical Society A}. (Kenneth Shirley and Andrew Gelman) [2013] Deep interactions with MRP: Election turnout and voting patterns among small electoral subgroups. {\em American Journal of Political Science}. (Yair Ghitza and Andrew Gelman) [2013] Charles Murray’s {\em Coming Apart} and the measurement of social and political divisions. {\em Statistics, Politics and Policy}.
Introduction: The talk is at the University of Amsterdam in the Diamantbeurs (Weesperplein 4, Amsterdam), room 5.01, at noon. Here’s the plan: Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up? In recent years, psychology and medicine have been rocked by scandals of research fraud. At the same time, there is a growing awareness of serious flaws in the general practices of statistics for scientific research, to the extent that top journals routinely publish claims that are implausible and cannot be replicated. All this is occurring despite (or perhaps because of?) statistical tools such as Type 1 error control that are supposed to restrict the rate of unreliable claims. We consider ways in which prior information and Bayesian methods might help resolve these problems. I don’t know how organized this talk will be. It combines a bunch of ideas that have been floating around recently. Here are a few recent articles that
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Introduction: “Ich glaube, dass die Wahrscheinlichkeitsrechnung das richtige Werkzeug zum Lösen solcher Probleme ist”, sagt Andrew Gelman , Statistikprofessor von der Columbia-Universität in New York. Wie oft aber derart knifflige Aufgaben im realen Leben auftauchen, könne er nicht sagen. Was fast schon beruhigend klingt. OK, fine.
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Introduction: 15-2040 != 19-3010 (and, for that matter, 25-1022 != 25-1063).
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Introduction: I checked and somebody went in and screwed up my fixes to the wikipedia page on Bayesian inference. I give up.
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Introduction: Malecki asks: Is this the worst infographic ever to appear in NYT? USA Today is not something to aspire to. To connect to some of our recent themes , I agree this is a pretty horrible data display. But it’s not bad as a series of images. Considering the competition to be a cartoon or series of photos, these images aren’t so bad. One issue, I think, is that designers get credit for creativity and originality (unusual color combinations! Histogram bars shaped like mosques!) , which is often the opposite of what we want in a clear graph. It’s Martin Amis vs. George Orwell all over again.
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Introduction: Cathy O’Neil organized this visualization project with NYPD stop-and-frisk data. It’s part of the Data Without Borders project. Unfortunately, because of legal restrictions I couldn’t send them the data Jeff, Alex, and I used in our project several years ago.
5 0.90369081 398 andrew gelman stats-2010-11-06-Quote of the day
Introduction: “A statistical model is usually taken to be summarized by a likelihood, or a likelihood and a prior distribution, but we go an extra step by noting that the parameters of a model are typically batched, and we take this batching as an essential part of the model.”
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