andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2157 knowledge-graph by maker-knowledge-mining
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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}.
sentIndex sentText sentNum sentScore
1 (Jonathan Kropko, Ben Goodrich, Andrew Gelman, and Jennifer Hill) Bayesian nonparametric weighted sampling inference. [sent-18, score-0.03]
2 Norris, and Sandro Galea) Statistical graphics: [2013] Infovis and statistical graphics: Different goals, different looks (with discussion). [sent-21, score-0.05]
3 (Andrew Gelman and Antony Unwin) Tradeoffs in information graphics (rejoinder to discussion). [sent-23, score-0.033]
4 (Andrew Gelman and Antony Unwin) Bayesian methods: [2013] A nondegenerate estimator for hierarchical variance parameters via penalized likelihood estimation. [sent-24, score-0.031]
5 (Andrew Gelman) [2014] How Bayesian analysis cracked the red-state, blue-state problem. [sent-32, score-0.031]
6 (Andrew Gelman and Michael Betancourt) Revised evidence for statistical standards. [sent-36, score-0.05]
7 (Wei Wang and Andrew Gelman) Weakly informative prior for point estimation of covariance matrices in hierarchical models. [sent-38, score-0.031]
8 (Andrew Gelman) [2013] To throw away data: Plagiarism as a statistical crime. [sent-52, score-0.05]
9 (Andrew Gelman and Mark Palko) History and philosophy of statistics: [2013] To throw away data: Plagiarism as a statistical crime. [sent-58, score-0.05]
10 (Andrew Gelman) [2013] “Not only defended but also applied”: The perceived absurdity of Bayesian inference (with discussion). [sent-64, score-0.03]
11 (Andrew Gelman and Christian Robert) [2013] Philosophy and the practice of Bayesian statistics (with discussion). [sent-67, score-0.045]
12 Tochterman, Karen Johnson, and Alan Felix) Centralized analysis of local data, with dollars and lives on the line: Lessons from the home radon experience. [sent-89, score-0.031]
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same-blog 1 1.0000005 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
3 0.73176229 2034 andrew gelman stats-2013-09-23-My talk Tues 24 Sept at 12h30 at Université de Technologie de Compiègne
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)
4 0.19905883 169 andrew gelman stats-2010-07-29-Say again?
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.
Introduction: From August 1990. It was in the form of a note sent to all the people in the statistics group of Bell Labs, where I’d worked that summer. To all: Here’s the abstract of the work I’ve done this summer. It’s stored in the file, /fs5/gelman/abstract.bell, and copies of the Figures 1-3 are on Trevor’s desk. Any comments are of course appreciated; I’m at gelman@stat.berkeley.edu. On the Routine Use of Markov Chains for Simulation Andrew Gelman and Donald Rubin, 6 August 1990 corrected version: 8 August 1990 1. Simulation In probability and statistics we can often specify multivariate distributions many of whose properties we do not fully understand–perhaps, as in the Ising model of statistical physics, we can write the joint density function, up to a multiplicative constant that cannot be expressed in closed form. For an example in statistics, consider the Normal random effects model in the analysis of variance, which can be easily placed in a Bayesian fram
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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.
4 0.82308441 2034 andrew gelman stats-2013-09-23-My talk Tues 24 Sept at 12h30 at Université de Technologie de Compiègne
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)
Introduction: I was given the opportunity to briefly comment on the paper , A Bayesian approach to complex clinical diagnoses: a case-study in child abuse, by Nicky Best, Deborah Ashby, Frank Dunstan, David Foreman, and Neil McIntosh, for the Journal of the Royal Statistical Society. Here is what I wrote: Best et al. are working on an important applied problem and I have no reason to doubt that their approach is a step forward beyond diagnostic criteria based on point estimation. An attempt at an accurate assessment of variation is important not just for statistical reasons but also because scientists have the duty to convey their uncertainty to the larger world. I am thinking, for example, of discredited claims such as that of the mathematician who claimed to predict divorces with 93% accuracy (Abraham, 2010). Regarding the paper at hand, I thought I would try an experiment in comment-writing. My usual practice is to read the graphs and then go back and clarify any questions through the t
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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: Rick Wash writes: A colleague as USC (Lian Jian) and I were recently discussing a statistical analysis issue that both of us have run into recently. We both mostly do research about how people use online interactive websites. One property that most of these systems have is known as the “powerlaw of participation” — the distribution of the number of contributions from each person follows a powerlaw. This mean that a few people contribution a TON and many, many people are in the “long tail” and contribute very rarely. For example, Facebook posts and twitter posts both have this distribution, as do comments on blogs and many other forms of user contribution online. This distribution has proven to be a problem when we analyze individual behavior. The basic problem is that we’d like to account for the fact that we have repeated data from many users, but a large number of users only have 1 or 2 data points. For example, Lian recently analyzed data about monetary contributions
4 0.94888926 1808 andrew gelman stats-2013-04-17-Excel-bashing
Introduction: In response to the latest controversy , a statistics professor writes: It’s somewhat surprising to see Very Serious Researchers (apologies to Paul Krugman) using Excel. Some years ago, I was consulting on a trademark infringement case and was trying (unsuccessfully) to replicate another expert’s regression analysis. It wasn’t until I had the brainstorm to use Excel that I was able to reproduce his results – it may be better now, but at the time, Excel could propagate round-off error and catastrophically cancel like no other software! Microsoft has lots of top researchers so it’s hard for me to understand how Excel can remain so crappy. I mean, sure, I understand in some general way that they have a large user base, it’s hard to maintain backward compatibility, there’s feature creep, and, besides all that, lots of people have different preferences in data analysis than I do. But still, it’s such a joke. Word has problems too, but I can see how these problems arise from its d
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Introduction: Marty McKee at Wolfram Research appears to have a very very stupid colleague. McKee wrote to Christian Robert: Your article, “Evidence and Evolution: A review”, caught the attention of one of my colleagues, who thought that it could be developed into an interesting Demonstration to add to the Wolfram Demonstrations Project. As Christian points out, adapting his book review into a computer demonstration would be quite a feat! I wonder what McKee’s colleague could be thinking? I recommend that Wolfram fire McKee’s colleague immediately: what an idiot! P.S. I’m not actually sure that McKee was the author of this email; I’m guessing this was the case because this other very similar email was written under his name. P.P.S. To head off the inevitable comments: Yes, yes, I know this is no big deal and I shouldn’t get bent out of shape about it. But . . . Wolfram Research has contributed such great things to the world, that I hate to think of them wasting any money paying
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