andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2310 knowledge-graph by maker-knowledge-mining
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Introduction: Mon : Crowdstorming a dataset Tues : Ken Rice presents a unifying approach to statistical inference and hypothesis testing Wed : The health policy innovation center: how best to move from pilot studies to large-scale practice? Thurs : Heller, Heller, and Gorfine on univariate and multivariate information measures Fri : Discovering general multidimensional associations Sat : “The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)” Sun : Honored oldsters write about statistics
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same-blog 1 1.0 2310 andrew gelman stats-2014-04-28-On deck this week
Introduction: Mon : Crowdstorming a dataset Tues : Ken Rice presents a unifying approach to statistical inference and hypothesis testing Wed : The health policy innovation center: how best to move from pilot studies to large-scale practice? Thurs : Heller, Heller, and Gorfine on univariate and multivariate information measures Fri : Discovering general multidimensional associations Sat : “The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)” Sun : Honored oldsters write about statistics
2 0.42215258 2264 andrew gelman stats-2014-03-24-On deck this month
Introduction: Actually, more like the next month and a half . . . I just have this long backlog so I thought I might as well share it with you: Empirical implications of Empirical Implications of Theoretical Models A statistical graphics course and statistical graphics advice What property is important in a risk prediction model? Discrimination or calibration? Beyond the Valley of the Trolls Science tells us that fast food lovers are more likely to marry other fast food lovers References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling Adjudicating between alternative interpretations of a statistical interaction? The most-cited statistics papers ever American Psychological Society announces a new journal Am I too negative? As the boldest experiment in journalism history, you admit you made a mistake Personally, I’d rather go with Teragram Bizarre academic spam An old discussion of food deserts Skepticism about a published cl
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Introduction: Mon: I hate polynomials Tues: Spring forward, fall back, drop dead? Wed: Bayes in the research conversation Thurs: The health policy innovation center: how best to move from pilot studies to large-scale practice? Fri: Stroopy names Sat: He’s not so great in math but wants to do statistics and machine learning Sun: Comparing the full model to the partial model
4 0.29578829 2324 andrew gelman stats-2014-05-07-Once more on nonparametric measures of mutual information
Introduction: Ben Murell writes: Our reply to Kinney and Atwal has come out (http://www.pnas.org/content/early/2014/04/29/1403623111.full.pdf) along with their response (http://www.pnas.org/content/early/2014/04/29/1404661111.full.pdf). I feel like they somewhat missed the point. If you’re still interested in this line of discussion, feel free to post, and maybe the Murrells and Kinney can bash it out in your comments! Background: Too many MC’s not enough MIC’s, or What principles should govern attempts to summarize bivariate associations in large multivariate datasets? Heller, Heller, and Gorfine on univariate and multivariate information measures Kinney and Atwal on the maximal information coefficient Mr. Pearson, meet Mr. Mandelbrot: Detecting Novel Associations in Large Data Sets Gorfine, Heller, Heller, Simon, and Tibshirani don’t like MIC The fun thing is that all these people are sending me their papers, and I’m enough of an outsider in this field that each of the
5 0.19859193 2339 andrew gelman stats-2014-05-19-On deck this week
Introduction: Mon: My short career as a Freud expert Tues: “P.S. Is anyone working on hierarchical survival models?” Wed: Skepticism about a published claim regarding income inequality and happiness Thurs: Big Data needs Big Model Fri: Did Neyman really say of Fisher’s work, “It’s easy to get the right answer if you never define what the question is,” and did Fisher really describe Neyman as “a theorem-proving poseur who wouldn’t recognized real data if it bit him in the ass” Sat: An interesting mosaic of a data programming course Sun: Why I decided not to be a physicist
7 0.19394553 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me
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same-blog 1 0.97816902 2310 andrew gelman stats-2014-04-28-On deck this week
Introduction: Mon : Crowdstorming a dataset Tues : Ken Rice presents a unifying approach to statistical inference and hypothesis testing Wed : The health policy innovation center: how best to move from pilot studies to large-scale practice? Thurs : Heller, Heller, and Gorfine on univariate and multivariate information measures Fri : Discovering general multidimensional associations Sat : “The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)” Sun : Honored oldsters write about statistics
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Introduction: Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? Here’s a list of reasons why you should think again Thurs : An open site for researchers to post and share papers Fri : Questions about “Too Good to Be True” Sat : Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu Sun : White stripes and dead armadillos
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Introduction: Mon: Preregistration: what’s in it for you? Tues: What if I were to stop publishing in journals? Wed: Empirical implications of Empirical Implications of Theoretical Models Thurs: An Economist’s Guide to Visualizing Data Fri: The maximal information coefficient Sat: Problematic interpretations of confidence intervals Sun: The more you look, the more you find
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Introduction: Mon : Transitioning to Stan Tues : When you believe in things that you don’t understand Wed : Looking for Bayesian expertise in India, for the purpose of analysis of sarcoma trials Thurs : If you get to the point of asking, just do it. But some difficulties do arise . . . Fri : One-tailed or two-tailed? Sat : Index or indicator variables Sun : Fooled by randomness
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Introduction: Mon : The most-cited statistics papers ever Tues : American Psychological Society announces a new journal Wed : Am I too negative? Thurs : As the boldest experiment in journalism history, you admit you made a mistake Fri : The Notorious N.H.S.T. presents: Mo P-values Mo Problems Sat : Bizarre academic spam Sun : An old discussion of food deserts
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same-blog 1 0.94674927 2310 andrew gelman stats-2014-04-28-On deck this week
Introduction: Mon : Crowdstorming a dataset Tues : Ken Rice presents a unifying approach to statistical inference and hypothesis testing Wed : The health policy innovation center: how best to move from pilot studies to large-scale practice? Thurs : Heller, Heller, and Gorfine on univariate and multivariate information measures Fri : Discovering general multidimensional associations Sat : “The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)” Sun : Honored oldsters write about statistics
Introduction: Paul Alper writes: You recently posted on graphs and how to convey information. I don’t believe you have ever posted anything on this dynamite randomized clinical trial of 90,000 (!!) 40-59 year-old women over a 25-year period (also !!). The graphs below are figures 2, 3 and 4 respectively, of http://www.bmj.com/content/348/bmj.g366 The control was physical exam only and the treatment was physical exam plus mammography. The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment). Note the superfluousness of the p-values. There is an accompanying editorial in the BMJ http://www.bmj.com/content/348/bmj.g1403 which refers to “vested interests” which can override any statistics, no matter how striking: We agree with Miller and colleagues that “the rationale for screening by mammography be urgently reassessed by policy makers.” As time goes
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
4 0.77294654 2324 andrew gelman stats-2014-05-07-Once more on nonparametric measures of mutual information
Introduction: Ben Murell writes: Our reply to Kinney and Atwal has come out (http://www.pnas.org/content/early/2014/04/29/1403623111.full.pdf) along with their response (http://www.pnas.org/content/early/2014/04/29/1404661111.full.pdf). I feel like they somewhat missed the point. If you’re still interested in this line of discussion, feel free to post, and maybe the Murrells and Kinney can bash it out in your comments! Background: Too many MC’s not enough MIC’s, or What principles should govern attempts to summarize bivariate associations in large multivariate datasets? Heller, Heller, and Gorfine on univariate and multivariate information measures Kinney and Atwal on the maximal information coefficient Mr. Pearson, meet Mr. Mandelbrot: Detecting Novel Associations in Large Data Sets Gorfine, Heller, Heller, Simon, and Tibshirani don’t like MIC The fun thing is that all these people are sending me their papers, and I’m enough of an outsider in this field that each of the
5 0.77240086 221 andrew gelman stats-2010-08-21-Busted!
Introduction: I’m just glad that universities don’t sanction professors for publishing false theorems. If the guy really is nailed by the feds for fraud, I hope they don’t throw him in prison. In general, prison time seems like a brutal, expensive, and inefficient way to punish people. I’d prefer if the government just took 95% of his salary for several years, made him do community service (cleaning equipment at the local sewage treatment plant, perhaps; a lab scientist should be good at this sort of thing, no?), etc. If restriction of this dude’s personal freedom is judged be part of the sentence, he could be given some sort of electronic tag that would send a message to the police if he were ever more than 3 miles from his home. But no need to bill the taxpayers for the cost of keeping him in prison.
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