andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-840 knowledge-graph by maker-knowledge-mining
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Introduction: Jay Ulfelder writes: I see that you blogged about limitations of Bayesian model averaging. As it happens, I was also blogging about BMA, but with an example where it seems to be working reasonably well, at least for the narrow purpose of forecasting. The topic is the analysis I did for CFR earlier this year on nonviolent uprisings. I don’t have time to look into this one but I wanted to pass it on.
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1 Jay Ulfelder writes: I see that you blogged about limitations of Bayesian model averaging. [sent-1, score-0.627]
2 As it happens, I was also blogging about BMA, but with an example where it seems to be working reasonably well, at least for the narrow purpose of forecasting. [sent-2, score-1.285]
3 The topic is the analysis I did for CFR earlier this year on nonviolent uprisings. [sent-3, score-0.816]
4 I don’t have time to look into this one but I wanted to pass it on. [sent-4, score-0.572]
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same-blog 1 1.0 840 andrew gelman stats-2011-08-05-An example of Bayesian model averaging
Introduction: Jay Ulfelder writes: I see that you blogged about limitations of Bayesian model averaging. As it happens, I was also blogging about BMA, but with an example where it seems to be working reasonably well, at least for the narrow purpose of forecasting. The topic is the analysis I did for CFR earlier this year on nonviolent uprisings. I don’t have time to look into this one but I wanted to pass it on.
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Introduction: Nick Firoozye writes: I had a question about BMA [Bayesian model averaging] and model combinations in general, and direct it to you since they are a basic form of hierarchical model, albeit in the simplest of forms. I wanted to ask what the underlying assumptions are that could lead to BMA improving on a larger model. I know model combination is a topic of interest in the (frequentist) econometrics community (e.g., Bates & Granger, http://www.jstor.org/discover/10.2307/3008764?uid=3738032&uid;=2&uid;=4&sid;=21101948653381) but at the time it was considered a bit of a puzzle. Perhaps small models combined outperform a big model due to standard errors, insufficient data, etc. But I haven’t seen much in way of Bayesian justification. In simplest terms, you might have a joint density P(Y,theta_1,theta_2) from which you could use the two marginals P(Y,theta_1) and P(Y,theta_2) to derive two separate forecasts. A BMA-er would do a weighted average of the two forecast densities, having p
3 0.097225547 2368 andrew gelman stats-2014-06-11-Bayes in the research conversation
Introduction: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
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Introduction: Jay Ulfelder asks: I have a question for you about what to do in a situation where you have two measures of your dependent variable and no prior reasons to strongly favor one over the other. Here’s what brings this up: I’m working on a project with Michael Ross where we’re modeling transitions to and from democracy in countries worldwide since 1960 to estimate the effects of oil income on the likelihood of those events’ occurrence. We’ve got a TSCS data set, and we’re using a discrete-time event history design, splitting the sample by regime type at the start of each year and then using multilevel logistic regression models with parametric measures of time at risk and random intercepts at the country and region levels. (We’re also checking for the usefulness of random slopes for oil wealth at one or the other level and then including them if they improve a model’s goodness of fit.) All of this is being done in Stata with the gllamm module. Our problem is that we have two plausib
5 0.091905437 1874 andrew gelman stats-2013-05-28-Nostalgia
Introduction: Saw Argo the other day, was impressed by the way it was filmed in such a 70s style, sorta like that movie The Limey or an episode of the Rockford Files. I also felt nostalgia for that relatively nonviolent era. All those hostages and nobody was killed. It’s a good thing the Ayatollah didn’t have some fundamentalist Shiite equivalent of John Yoo telling him to waterboard everybody. At the time we were all so angry and upset about the hostage-taking, but from the perspective of our suicide-bomber era, that whole hostage episode seems so comfortingly mild.
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Introduction: Jay Ulfelder writes: I see that you blogged about limitations of Bayesian model averaging. As it happens, I was also blogging about BMA, but with an example where it seems to be working reasonably well, at least for the narrow purpose of forecasting. The topic is the analysis I did for CFR earlier this year on nonviolent uprisings. I don’t have time to look into this one but I wanted to pass it on.
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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
3 0.77757478 1719 andrew gelman stats-2013-02-11-Why waste time philosophizing?
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: 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: Every once in awhile I get a question that I can directly answer from my published research. When that happens it makes me so happy. Here’s an example. Patrick Lam wrote, Suppose one develops a Bayesian model to estimate a parameter theta. Now suppose one wants to evaluate the model via simulation by generating fake data where you know the value of theta and see how well you recover theta with your model, assuming that you use the posterior mean as the estimate. The traditional frequentist way of evaluating it might be to generate many datasets and see how well your estimator performs each time in terms of unbiasedness or mean squared error or something. But given that unbiasedness means nothing to a Bayesian and there is no repeated sampling interpretation in a Bayesian model, how would you suggest one would evaluate a Bayesian model? My reply: I actually have a paper on this ! It is by Cook, Gelman, and Rubin. The idea is to draw theta from the prior distribution.
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1 0.95714986 677 andrew gelman stats-2011-04-24-My NOAA story
Introduction: I recently learned we have some readers at the National Oceanic and Atmospheric Administration so I thought I’d share an old story. About 35 years ago my brother worked briefly as a clerk at NOAA in their D.C. (or maybe it was D.C.-area) office. His job was to enter the weather numbers that came in. He had a boss who was very orderly. At one point there was a hurricane that wiped out some weather station in the Caribbean, and his boss told him to put in the numbers anyway. My brother protested that they didn’t have the data, to which his boss replied: “I know what the numbers are.” Nowadays we call this sort of thing “imputation” and we like it. But not in the raw data! I bet nowadays they have an NA code.
2 0.92704028 211 andrew gelman stats-2010-08-17-Deducer update
Introduction: A year ago we blogged about Ian Fellows’s R Gui called Deducer (oops, my bad, I meant to link to this ). Fellows sends in this update: Since version 0.1, I [Fellows] have added: 1. A nice plug-in interface, so that people can extend Deducer’s capability without leaving the comfort of R. (see: http://www.deducer.org/pmwiki/pmwiki.php?n=Main.Development ) 2. Several new dialogs. 3. A one-step installer for windows. 4. A plug-in package (DeducerExtras) which extends the scope of analyses covered. 5. A plotting GUI that can create anything from simple histograms to complex custom graphics. Deducer is designed to be a free easy to use alternative to proprietary data analysis software such as SPSS, JMP, and Minitab. It has a menu system to do common data manipulation and analysis tasks, and an excel-like spreadsheet in which to view and edit data frames. The goal of the project is two fold. Provide an intuitive interface so that non-technical users can learn and p
3 0.90877497 1119 andrew gelman stats-2012-01-15-Excellence in Statistical Reporting Award
Introduction: The American Statistical Association is seeking nominations for its annual Excellence in Statistical Reporting Award . The award was created in 2004 to encourage and recognize members of the communications media who have best displayed an informed interest in the science of statistics and its role in public life. The award can be given for a single statistical article or for a body of work. Former winners of the award include: Felix Salmon , financial blogger, 2010; Sharon Begley , Newsweek, 2009; Mark Buchanan, New York Times, 2008; John Berry, Bloomberg News, 2005; and Gina Kolata, New York Times, 2004. If anyone has any suggestions for the 2012 award, feel free to post in the comments or email me.
4 0.89208281 189 andrew gelman stats-2010-08-06-Proposal for a moratorium on the use of the words “fashionable” and “trendy”
Introduction: Tyler Cowen links to an interesting article by Terry Teachout on David Mamet’s political conservatism. I don’t think of playwrights as gurus, but I do find it interesting to consider the political orientations of authors and celebrities . I have only one problem with Teachout’s thought-provoking article. He writes: As early as 2002 . . . Arguing that “the Western press [had] embraced antisemitism as the new black,” Mamet drew a sharp contrast between that trendy distaste for Jews and the harsh realities of daily life in Israel . . . In 2006, Mamet published a collection of essays called The Wicked Son: Anti-Semitism, Jewish Self-Hatred and the Jews that made the point even more bluntly. “The Jewish State,” he wrote, “has offered the Arab world peace since 1948; it has received war, and slaughter, and the rhetoric of annihilation.” He went on to argue that secularized Jews who “reject their birthright of ‘connection to the Divine’” succumb in time to a self-hatred tha
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