andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1234 knowledge-graph by maker-knowledge-mining
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Introduction: Ben Lauderdale and Tom Clark wrote a paper in which they modeled Supreme Court judges as having different relative positions on different issues. Here’s an illustration of what their model does: Cool. And I looove the graphs. I’m pretty sure that, from the effort that the authors put into creating the many displays in the article, came a much deeper understanding of their model and its political implications.
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same-blog 1 0.99999994 1234 andrew gelman stats-2012-03-28-The Supreme Court’s Many Median Justices
Introduction: Ben Lauderdale and Tom Clark wrote a paper in which they modeled Supreme Court judges as having different relative positions on different issues. Here’s an illustration of what their model does: Cool. And I looove the graphs. I’m pretty sure that, from the effort that the authors put into creating the many displays in the article, came a much deeper understanding of their model and its political implications.
2 0.24828795 235 andrew gelman stats-2010-08-25-Term Limits for the Supreme Court?
Introduction: In the wake of the confirmation of Elena Kagan to the Supreme Court, political commentators have been expressing a bit of frustration about polarization within the court and polarization in the nomination process. One proposal that’s been floating around is to replace lifetime appointments by fixed terms, perhaps twelve or eighteen years. This would enforce a regular schedule of replacements, instead of the current system in which eighty-something judges have an incentive to hang on as long as possible so as to time their retirements to be during the administration of a politically-compatible president. A couple weeks ago at the sister blog, John Sides discussed some recent research that was relevant to the judicial term limits proposal. Political scientists Justin Crowe and Chris Karpowitz analyzed the historical record or Supreme Court terms and found that long terms of twenty years or more have been happening since the early years of the court. Yes, there is less turnover th
Introduction: John Kastellec, Jeff Lax, and Justin Phillips write : Do senators respond to the preferences of their states’ median voters or only to the preferences of their co-partisans? We [Kastellec et al.] study responsiveness using roll call votes on ten recent Supreme Court nominations. We develop a method for estimating state-level public opinion broken down by partisanship. We find that senators respond more powerfully to their partisan base when casting such roll call votes. Indeed, when their state median voter and party median voter disagree, senators strongly favor the latter. [emphasis added] This has significant implications for the study of legislative responsiveness, the role of public opinion in shaping the personnel of the nations highest court, and the degree to which we should expect the Supreme Court to be counter-majoritarian. Our method can be applied elsewhere to estimate opinion by state and partisan group, or by many other typologies, so as to study other important qu
4 0.12587753 1241 andrew gelman stats-2012-04-02-Fixed effects and identification
Introduction: Tom Clark writes: Drew Linzer and I [Tom] have been working on a paper about the use of modeled (“random”) and unmodeled (“fixed”) effects. Not directly in response to the paper, but in conversations about the topic over the past few months, several people have said to us things to the effect of “I prefer fixed effects over random effects because I care about identification.” Neither Drew nor I has any idea what this comment is supposed to mean. Have you come across someone saying something like this? Do you have any thoughts about what these people could possibly mean? I want to respond to this concern when people raise it, but I have failed thus far to inquire what is meant and so do not know what to say. My reply: I have a “cultural” reply, which is that so-called fixed effects are thought to make fewer assumptions, and making fewer assumptions is considered a generally good thing that serious people do, and identification is considered a concern of serious people, so they g
5 0.11140989 26 andrew gelman stats-2010-05-11-Update on religious affiliations of Supreme Court justices
Introduction: When Sonia Sotomayor was nominated for the Supreme Court, and there was some discussion of having 6 Roman Catholics on the court at the same time, I posted the following historical graph: It’s time for an update: It’s still gonna take awhile for the Catholics to catch up. . . . And this one might be relevant too: It looks as if Jews and men have been overrepresented, also Episcopalians (which, as I noted earlier, are not necessarily considered Protestant in terms of religious doctrine but which I counted as such for the ethnic categorization). Religion is an interesting political variable because it’s nominally about religious belief but typically seems to be more about ethnicity.
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same-blog 1 0.95434755 1234 andrew gelman stats-2012-03-28-The Supreme Court’s Many Median Justices
Introduction: Ben Lauderdale and Tom Clark wrote a paper in which they modeled Supreme Court judges as having different relative positions on different issues. Here’s an illustration of what their model does: Cool. And I looove the graphs. I’m pretty sure that, from the effort that the authors put into creating the many displays in the article, came a much deeper understanding of their model and its political implications.
2 0.69275373 2133 andrew gelman stats-2013-12-13-Flexibility is good
Introduction: If I made a separate post for each interesting blog discussion, we’d get overwhelmed. That’s why I often leave detailed responses in the comments section, even though I’m pretty sure that most readers don’t look in the comments at all. Sometimes, though, I think it’s good to bring such discussions to light. Here’s a recent example. Michael wrote : Poor predictive performance usually indicates that the model isn’t sufficiently flexible to explain the data, and my understanding of the proper Bayesian strategy is to feed that back into your original model and try again until you achieve better performance. Corey replied : It was my impression that — in ML at least — poor predictive performance is more often due to the model being too flexible and fitting noise. And Rahul agreed : Good point. A very flexible model will describe your training data perfectly and then go bonkers when unleashed on wild data. But I wrote : Overfitting comes from a model being flex
3 0.67220891 24 andrew gelman stats-2010-05-09-Special journal issue on statistical methods for the social sciences
Introduction: Last year I spoke at a conference celebrating the 10th anniversary of the University of Washington’s Center for Statistics and the Social Sciences, and just today a special issue of the journal Statistical Methodology came out in honor of the center’s anniversary. My article in the special issue actually has nothing to do with my talk at the conference; rather, it’s an exploration of an idea that Iven Van Mechelen and I had for understanding deterministic models probabilistically: For the analysis of binary data, various deterministic models have been proposed, which are generally simpler to fit and easier to understand than probabilistic models. We claim that corresponding to any deterministic model is an implicit stochastic model in which the deterministic model fits imperfectly, with errors occurring at random. In the context of binary data, we consider a model in which the probability of error depends on the model prediction. We show how to fit this model using a stocha
Introduction: Mandelbrot on taxonomy (from 1955; the first publication about fractals that I know of): Searching for Mandelbrot on the blog led me to Akaike , who also recently passed away and also did interesting early work on self-similar stochastic processes. For example, this wonderful opening of his 1962 paper, “On a limiting process which asymptotically produces f^{-2} spectral density”: In the recent papers in which the results of the spectral analyses of roughnesses of runways or roadways are reported, the power spectral densities of approximately the form f^{-2} (f: frequency) are often treated. This fact directed the present author to the investigation of the limiting process which will provide the f^{-2} form under fairly general assumptions. In this paper a very simple model is given which explains a way how the f^{-2} form is obtained asymptotically. Our fundamental model is that the stochastic process, which might be considered to represent the roughness of the runway
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Introduction: A link from Simon Jackman’s blog led me to an article by James Heckman, Hedibert Lopes, and Remi Piatek from 2011, “Treatment effects: A Bayesian perspective.” I was pleasantly surprised to see this, partly because I didn’t know that Heckman was working on Bayesian methods, and partly because the paper explicitly refers to the “potential outcomes model,” a term I associate with Don Rubin. I’ve had the impression that Heckman and Rubin don’t like each other (I was a student of Rubin and have never met Heckman, so I’m only speaking at second hand here), so I was happy to see some convergence. I was curious how Heckman et al. would source the potential outcome model. They do not refer to Rubin’s 1974 paper or to Neyman’s 1923 paper (which was republished in 1990 and is now taken to be the founding document of the Neyman-Rubin approach to causal inference). Nor, for that matter, do Heckman et al. refer to the more recent developments of these theories by Robins, Pearl, and other
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same-blog 1 0.85776949 1234 andrew gelman stats-2012-03-28-The Supreme Court’s Many Median Justices
Introduction: Ben Lauderdale and Tom Clark wrote a paper in which they modeled Supreme Court judges as having different relative positions on different issues. Here’s an illustration of what their model does: Cool. And I looove the graphs. I’m pretty sure that, from the effort that the authors put into creating the many displays in the article, came a much deeper understanding of their model and its political implications.
2 0.81879652 2363 andrew gelman stats-2014-06-07-“Does researching casual marijuana use cause brain abnormalities?”
Introduction: David Austin points me to a wonderfully-titled post by Lior Pachter criticizing a recent paper on the purported effects of cannabis use. Not the paper criticized here . Someone should send this all to David Brooks. I’ve heard he’s interested in the latest scientific findings, and I know he’s interested in marijuana.
3 0.79348767 332 andrew gelman stats-2010-10-10-Proposed new section of the American Statistical Association on Imaging Sciences
Introduction: Martin Lindquist writes that he and others are trying to start a new ASA section on statistics in imaging. If you’re interested in being a signatory to its formation, please send him an email.
4 0.75388944 1088 andrew gelman stats-2011-12-28-Argument in favor of Ddulites
Introduction: Mark Palko defines a Ddulite as follows: A preference for higher tech solutions even in cases where lower tech alternatives have greater and more appropriate functionality; a person of ddulite tendencies. Though Ddulites are the opposite of Luddites with respect to attitudes toward technology, they occupy more or less the same point with respect to functionality. As a sometime Luddite myself (no cell phone, tv, microwave oven, etc.), I should in fairness point out the logic in favor of being a Ddulite. Old technology is typically pretty stable; new technology is improving. It can make sense to switch early (before the new technology actually performs better than the old) to get the benefits of being familiar with the new technology once it does take off.
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Introduction: José Iparraguirre writes: There’s a letter in the latest issue of The Economist (July 31st) signed by Sir Richard Branson (Virgin), Michael Masters (Masters Capital Management) and David Frenk (Better Markets) about an “>OECD report on speculation and the prices of commodities, which includes the following: “The report uses a Granger causality test to measure the relationship between the level of commodities futures contracts held by swap dealers, and the prices of those commodities. Granger tests, however, are of dubious applicability to extremely volatile variables like commodities prices.” The report says: Granger causality is a standard statistical technique for determining whether one time series is useful in forecasting another. It is important to bear in mind that the term causality is used in a statistical sense, and not in a philosophical one of structural causation. More precisely a variable A is said to Granger cause B if knowing the time paths of B and A toge
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