andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1069 knowledge-graph by maker-knowledge-mining
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Introduction: I’m talkin bout this .
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same-blog 1 1.0 1069 andrew gelman stats-2011-12-19-I got one of these letters once and was so irritated that I wrote back to the journal withdrawing my paper
Introduction: I’m talkin bout this .
2 0.15326211 1171 andrew gelman stats-2012-02-16-“False-positive psychology”
Introduction: Everybody’s talkin bout this paper by Joseph Simmons, Leif Nelson and Uri Simonsohn, who write : Despite empirical psychologists’ nominal endorsement of a low rate of false-positive findings (≤ .05), flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates. In many cases, a researcher is more likely to falsely find evidence that an effect exists than to correctly find evidence that it does not. We [Simmons, Nelson, and Simonsohn] present computer simulations and a pair of actual experiments that demonstrate how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis. Second, we suggest a simple, low-cost, and straightforwardly effective disclosure-based solution to this problem. The solution involves six concrete requirements for authors and four guidelines for reviewers, all of which impose a minimal burden on the publication process. Whatever you think about these recommend
3 0.074844211 610 andrew gelman stats-2011-03-13-Secret weapon with rare events
Introduction: Gregory Eady writes: I’m working on a paper examining the effect of superpower alliance on a binary DV (war). I hypothesize that the size of the effect is much higher during the Cold War than it is afterwards. I’m going to run a Chow test to check whether this effect differs significantly between 1960-1989 and 1990-2007 (Scott Long also has a method using predicted probabilities), but I’d also like to show the trend graphically, and thought that your “Secret Weapon” would be useful here. I wonder if there is anything I should be concerned about when doing this with a (rare-events) logistic regression. I was thinking to graph the coefficients in 5-year periods, moving a single year at a time (1960-64, 1961-65, 1962-66, and so on), reporting the coefficient in the graph for the middle year of each 5-year range). My reply: I don’t know nuthin bout no Chow test but, sure, I’d think the secret weapon would work. If you’re analyzing 5-year periods, it might be cleaner just to keep t
Introduction: Writing in the Washington Post, Matt Miller wants a billionaire to run for president and “save the country.” We already have two billionaires running for president. (OK, not really. Romney has a mere quarter of a billion bucks, and it’s Huntsman’s dad, not Huntsman himself, who’s the billionaire in that family.) And, according to all reports, NYC mayor Bloomberg would run for president in an instant if he thought he’d have a chance of winning. So we should amend Miller’s article to say that he wants a billionaire presidential candidate who (a) shares the political views of a “senior fellow at the Center for American Progress and co-host of public radio’s “Left, Right, and Center” and (b) has a chance of winning. That shouldn’t be too hard to find, right? Hey, I have an idea! MIller writes that that Thomas Friedman just wrote a book arguing that “the right independent candidacy could provide for our dysfunctional politics presents an unrivaled opportunity.” Friedman’s actu
Introduction: Stephen Olivier points me to this horrible, horrible news article by Jonathan Haidt, “Why working-class people vote conservative”: Across the world, blue-collar voters ally themselves with the political right . . . Why on Earth would a working-class person ever vote for a conservative candidate? This question has obsessed the American left since Ronald Reagan first captured the votes of so many union members, farmers, urban Catholics and other relatively powerless people – the so-called “Reagan Democrats”. . . . Sorry, but no no no no no. Where to start? Here’s the difference between upper-income and lower-income votes in presidential elections: Ronald Reagan did about 20 percentage points better among voters in the upper third of income, compared to voters in the lower third. The relation between income and voting since 1980 is about the same as it was in the 1940s. Oh yeah, Haidt said something about “across the world.” How bout this: It varies. In mos
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Introduction: I’m talkin bout this .
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Introduction: Details here .
3 0.39649147 734 andrew gelman stats-2011-05-28-Funniest comment ever
Introduction: Here (scroll down to the bottom; for some reason the link doesn’t go directly to the comment itself). I’ve never actually seen a Kaypro but I remember the ads. (Background here .)
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Introduction: It’s not what you might think.
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Introduction: I wouldn’t go that far, but I’ll send along this article by Ahti-Veikko Pietarinen that was sent to me by Lee Sechrest. Those of you who like this sort of thing might like this sort of thing. I neither endorse nor anti-endorse. Or, I should say, I am in sympathy with the author’s general attitude but I have not looked at any of the details.
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Introduction: I’m talkin bout this .
Introduction: Marc Tanguay writes in with a specific question that has a very general answer. First, the question: I [Tanguay] am currently running a MCMC for which I have 3 parameters that are restricted to a specific space. 2 are bounded between 0 and 1 while the third is binary and updated by a Beta-Binomial. Since my priors are also bounded, I notice that, conditional on All the rest (which covers both data and other parameters), the density was not varying a lot within the space of the parameters. As a result, the acceptance rate is high, about 85%, and this despite the fact that all the parameter’s space is explore. Since in your book, the optimal acceptance rates prescribed are lower that 50% (in case of multiple parameters), do you think I should worry about getting 85%. Or is this normal given the restrictions on the parameters? First off: Yes, my guess is that you should be taking bigger jumps. 85% seems like too high an acceptance rate for Metropolis jumping. More generally, t
3 0.45592445 1638 andrew gelman stats-2012-12-25-Diving chess
Introduction: Knowing of my interest in Turing run-around-the-house chess , David Lockhart points me to this : Diving Chess is a chess variant, which is played in a swimming pool. Instead of using chess clocks, each player must submerge themselves underwater during their turn, only to resurface when they are ready to make a move. Players must make a move within 5 seconds of resurfacing (they will receive a warning if not, and three warnings will result in a forfeit). Diving Chess was invented by American Chess Master Etan Ilfeld; the very first exhibition game took place between Ilfeld and former British Chess Champion William Hartston at the Thirdspace gym in Soho on August 2nd, 2011. Hartston won the match which lasted almost two hours such that each player was underwater for an entire hour.
4 0.36280257 1576 andrew gelman stats-2012-11-13-Stan at NIPS 2012 Workshop on Probabilistic Programming
Introduction: If you need an excuse to go skiing in Tahoe next month, our paper on Stan as a probabilistic programming language was accepted for: Workshop on Probabilistic Programming NIPS 2012 7–8 December, 2012, Lake Tahoe, Nevada The workshop is organized by the folks behind the probabilistic programming language Church and has a great lineup of invited speakers (Chris Bishop, Josh Tennenbaum, and Stuart Russell). And in case you’re interested in the main conference, here’s the list of accepted NIPS 2012 papers and posters . To learn more about Stan, check out the links to the manual on the Stan Home Page We’ll put up a link to our final NIPS workshop paper there when we finish it.
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Introduction: We just released Stan 1.1.1 and RStan 1.1.1 As usual, you can find download and install instructions at: http://mc-stan.org/ This is a patch release and is fully backward compatible with Stan and RStan 1.1.0. The main thing you should notice is that the multivariate models should be much faster and all the bugs reported for 1.1.0 have been fixed. We’ve also added a bit more functionality. The substantial changes are listed in the following release notes. v1.1.1 (5 February 2012) ====================================================================== Bug Fixes ———————————- * fixed bug in comparison operators, which swapped operator< with operator<= and swapped operator> with operator>= semantics * auto-initialize all variables to prevent segfaults * atan2 gradient propagation fixed * fixed off-by-one in NUTS treedepth bound so NUTS goes at most to specified tree depth rather than specified depth + 1 * various compiler compatibility and minor consistency issues * f
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