andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1794 knowledge-graph by maker-knowledge-mining
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Introduction: U.S. Treasury, Office of Financial Research, Tues 9 Apr afternoon (I don’t actually know exactly when or in what room): Parameterization and Bayesian Modeling — Johns Hopkins University, Department of Biostatistics, 4pm Wed 10 Apr, room W2030 School of Public Health : Little data: How traditional statistical ideas remain relevant in a big-data world At the end of the day, after all the processing, big data are being used to answer little- data questions such as, Does an observed pattern generalize to the larger population?, or Could it be explained by alternative processes (sometimes called “chance”)? We discuss some recent ideas in the world of “little data” that remain of big importance.
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same-blog 1 1.0 1794 andrew gelman stats-2013-04-09-My talks in DC and Baltimore this week
Introduction: U.S. Treasury, Office of Financial Research, Tues 9 Apr afternoon (I don’t actually know exactly when or in what room): Parameterization and Bayesian Modeling — Johns Hopkins University, Department of Biostatistics, 4pm Wed 10 Apr, room W2030 School of Public Health : Little data: How traditional statistical ideas remain relevant in a big-data world At the end of the day, after all the processing, big data are being used to answer little- data questions such as, Does an observed pattern generalize to the larger population?, or Could it be explained by alternative processes (sometimes called “chance”)? We discuss some recent ideas in the world of “little data” that remain of big importance.
Introduction: Liz Stuart sends this one along: Johns Hopkins University Post-Doctoral Fellow Opening The Department of Mental Health invites applications for a post-doctoral research fellow in Statistical Methods for Mental Health Research, to be supervised by Dr. Elizabeth Stuart. The successful applicant for this position will work on an NIH funded study to develop and evaluate propensity score methods when the covariates are measured with error, or when datasets contain similar, but not identical, measures of covariates of interest. The post-doc will work with a team of researchers on development of new statistical methods, analysis of existing data, Monte Carlo simulations, and will take the lead on scientific publications. Opportunities will be provided for training in causal inference, missing data, latent variable methods, the evaluation of statistical methods, and the application of statistics to mental health and education. The three particular motivating examples will be: earl
3 0.18300302 679 andrew gelman stats-2011-04-25-My talk at Stanford on Tuesday
Introduction: Of Beauty, Sex, and Power: Statistical Challenges in Estimating Small Effects. Tues 26 Apr, 12-1 in the Graham Stuart Lounge, 4th Floor, Encina West.
Introduction: See if you can interpolate the talk from the slides . The background is: I was invited to speak in this seminar on “big data.” I said I didn’t know anything about big data, I worked on little data. They said that was ok. Actually it was probably a crowd-pleasing move to tell these people that little-data ideas remain relevant.
5 0.15084064 1821 andrew gelman stats-2013-04-24-My talk midtown this Friday noon (and at Columbia Monday afternoon)
Introduction: At the City University of New York Graduate Center, 365 Fifth Avenue (between 34th and 35th street), room 6002. The topic: causality and statistical learning . Announcement is here (scroll down). It says that if you would like to attend any event, please respond by emailing datamining@gc.cuny.edu I’m also giving a shorter talk on the same topic in the Sustainable Development Seminar Series 4pm Monday 29 Apr in room 407 International Affairs Bldg (at 118th St. and Amsterdam Ave.). It’s just a coincidence that I’m giving the same talk twice. I was asked at different times to speak for these groups. When someone asks me to speak, I let them pick from recent talks on the list .
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7 0.10960023 546 andrew gelman stats-2011-01-31-Infovis vs. statistical graphics: My talk tomorrow (Tues) 1pm at Columbia
9 0.10365673 651 andrew gelman stats-2011-04-06-My talk at Northwestern University tomorrow (Thursday)
10 0.094596326 2207 andrew gelman stats-2014-02-11-My talks in Bristol this Wed and London this Thurs
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13 0.087006971 1499 andrew gelman stats-2012-09-16-Uri Simonsohn is speaking at Columbia tomorrow (Mon)
15 0.085360862 972 andrew gelman stats-2011-10-25-How do you interpret standard errors from a regression fit to the entire population?
16 0.083497517 453 andrew gelman stats-2010-12-07-Biostatistics via Pragmatic and Perceptive Bayes.
17 0.081720695 817 andrew gelman stats-2011-07-23-New blog home
18 0.079997562 465 andrew gelman stats-2010-12-13-$3M health care prediction challenge
19 0.079884306 2222 andrew gelman stats-2014-02-24-On deck this week
20 0.079812303 267 andrew gelman stats-2010-09-09-This Friday afternoon: Applied Statistics Center mini-conference on risk perception
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Introduction: U.S. Treasury, Office of Financial Research, Tues 9 Apr afternoon (I don’t actually know exactly when or in what room): Parameterization and Bayesian Modeling — Johns Hopkins University, Department of Biostatistics, 4pm Wed 10 Apr, room W2030 School of Public Health : Little data: How traditional statistical ideas remain relevant in a big-data world At the end of the day, after all the processing, big data are being used to answer little- data questions such as, Does an observed pattern generalize to the larger population?, or Could it be explained by alternative processes (sometimes called “chance”)? We discuss some recent ideas in the world of “little data” that remain of big importance.
2 0.71964824 2285 andrew gelman stats-2014-04-07-On deck this week
Introduction: Mon : How literature is like statistical reasoning: Kosara on stories. Gelman and Basbøll on stories. Tues : Understanding Simpson’s paradox using a graph Wed : Advice: positive-sum, zero-sum, or negative-sum Thurs : Small multiples of lineplots > maps (ok, not always, but yes in this case) Fri : “More research from the lunatic fringe” Sat : “Schools of statistical thoughts are sometimes jokingly likened to religions. This analogy is not perfect—unlike religions, statistical methods have no supernatural content and make essentially no demands on our personal lives. Looking at the comparison from the other direction, it is possible to be agnostic, atheistic, or simply live one’s life without religion, but it is not really possible to do statistics without some philosophy.” Sun : I was wrong . . .
3 0.71180403 2321 andrew gelman stats-2014-05-05-On deck this week
Introduction: Mon: Can we make better graphs of global temperature history? Tues: Priors I don’t believe Wed: Cause he thinks he’s so-phisticated Thurs: Discussion with Steven Pinker on research that is attached to data that are so noisy as to be essentially uninformative Fri: Combining forecasts: Evidence on the relative accuracy of the simple average and Bayesian model averaging for predicting social science problems Sat: What property is important in a risk prediction model? Discrimination or calibration? Sun: “What should you talk about?” Plus whatever the co-bloggers want to throw in. Right now I’m super-excited about wedge sampling but I’ll let you know more about that once the paper is done.
4 0.7035436 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
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15 0.60968184 1343 andrew gelman stats-2012-05-25-And now, here’s something we hope you’ll really like
18 0.60113746 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me
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1 0.97785819 1081 andrew gelman stats-2011-12-24-Statistical ethics violation
Introduction: A colleague writes: When I was in NYC I went to this party by group of Japanese bio-scientists. There, one guy told me about how the biggest pharmaceutical company in Japan did their statistics. They ran 100 different tests and reported the most significant one. (This was in 2006 and he said they stopped doing this few years back so they were doing this until pretty recently…) I’m not sure if this was 100 multiple comparison or 100 different kinds of test but I’m sure they wouldn’t want to disclose their data… Ouch!
2 0.97663069 908 andrew gelman stats-2011-09-14-Type M errors in the lab
Introduction: Jeff points us to this news article by Asher Mullard: Bayer halts nearly two-thirds of its target-validation projects because in-house experimental findings fail to match up with published literature claims, finds a first-of-a-kind analysis on data irreproducibility. An unspoken industry rule alleges that at least 50% of published studies from academic laboratories cannot be repeated in an industrial setting, wrote venture capitalist Bruce Booth in a recent blog post. A first-of-a-kind analysis of Bayer’s internal efforts to validate ‘new drug target’ claims now not only supports this view but suggests that 50% may be an underestimate; the company’s in-house experimental data do not match literature claims in 65% of target-validation projects, leading to project discontinuation. . . . Khusru Asadullah, Head of Target Discovery at Bayer, and his colleagues looked back at 67 target-validation projects, covering the majority of Bayer’s work in oncology, women’s health and cardiov
3 0.96815038 834 andrew gelman stats-2011-08-01-I owe it all to the haters
Introduction: Sometimes when I submit an article to a journal it is accepted right away or with minor alterations. But many of my favorite articles were rejected or had to go through an exhausting series of revisions. For example, this influential article had a very hostile referee and we had to seriously push the journal editor to accept it. This one was rejected by one or two journals before finally appearing with discussion. This paper was rejected by the American Political Science Review with no chance of revision and we had to publish it in the British Journal of Political Science, which was a bit odd given that the article was 100% about American politics. And when I submitted this instant classic (actually at the invitation of the editor), the referees found it to be trivial, and the editor did me the favor of publishing it but only by officially labeling it as a discussion of another article that appeared in the same issue. Some of my most influential papers were accepted right
4 0.9582417 1541 andrew gelman stats-2012-10-19-Statistical discrimination again
Introduction: Mark Johnstone writes: I’ve recently been investigating a new European Court of Justice ruling on insurance calculations (on behalf of MoneySuperMarket) and I found something related to statistics that caught my attention. . . . The ruling (which comes into effect in December 2012) states that insurers in Europe can no longer provide different premiums based on gender. Despite the fact that women are statistically safer drivers, unless it’s biologically proven there is a causal relationship between being female and being a safer driver, this is now seen as an act of discrimination (more on this from the Wall Street Journal). However, where do you stop with this? What about age? What about other factors? And what does this mean for the application of statistics in general? Is it inherently unjust in this context? One proposal has been to fit ‘black boxes’ into cars so more individual data can be collected, as opposed to relying heavily on aggregates. For fans of data and s
5 0.95288396 439 andrew gelman stats-2010-11-30-Of psychology research and investment tips
Introduction: A few days after “ Dramatic study shows participants are affected by psychological phenomena from the future ,” (see here ) the British Psychological Society follows up with “ Can psychology help combat pseudoscience? .” Somehow I’m reminded of that bit of financial advice which says, if you want to save some money, your best investment is to pay off your credit card bills.
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19 0.88389844 1779 andrew gelman stats-2013-03-27-“Two Dogmas of Strong Objective Bayesianism”