andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1821 knowledge-graph by maker-knowledge-mining
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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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same-blog 1 1.0 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 .
2 0.15084064 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: That’s the title of my talk at Montana State University this Thursday (21 Mar). For those of you who happen to be in the area, it’s 3:30-5:00pm in the Procrastinator Theater. I’m also speaking in the statistics seminar from 11-12:15 in the Byker Auditorium in the Chemistry building. Topic: Causality and Statistical Learning . P.S. My title is a bit Geng-inspired, which reminds me that I recently came across this interesting mini-bio.
4 0.12955038 1039 andrew gelman stats-2011-12-02-I just flew in from the econ seminar, and boy are my arms tired
Introduction: I’ve heard all sorts of scare stories of what it’s like to speak in an academic economics seminar: they’re rude, they interrupt constantly, they don’t let you get through three slides in an hour, etc. But whenever I’ve actually spoke in an economics department, the people have been polite and well-behaved, really it’s been like any other seminar. I mentioned this to some people awhile ago and they said that the nasty-economist thing only happens in the top departments. I’d spoken at Columbia (which, if not at the very top, is still respectable), but that was in the political economy seminar and I’m a political scientist, so maybe they were nice to me because I’m local. And the other econ departments where I’d spoken were in Europe (maybe they’re nicer there) or at non-elite institutions in the U.S. So I called my friend at Harvard econ, told him my story, and asked if I could speak there. He duly booked me for the Harvard-MIT econometrics seminar. I spoke at the seminar,
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.
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11 0.096985787 1451 andrew gelman stats-2012-08-08-Robert Kosara reviews Ed Tufte’s short course
12 0.094472617 651 andrew gelman stats-2011-04-06-My talk at Northwestern University tomorrow (Thursday)
13 0.094389938 1297 andrew gelman stats-2012-05-03-New New York data research organizations
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18 0.084701613 699 andrew gelman stats-2011-05-06-Another stereotype demolished
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same-blog 1 0.98352557 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 .
2 0.75036579 1143 andrew gelman stats-2012-01-29-G+ > Skype
Introduction: I spoke at the University of Kansas the other day. Kansas is far away so I gave the talk by video. We did it using a G+ hangout, and it worked really well, much much better than when I gave a talk via Skype . With G+, I could see and hear the audience clearly, and they could hear me just fine while seeing my slides (or my face, I went back and forth). Not as good as a live presentation but pretty good, considering. P.S. And here’s how to do it! Conflict of interest disclaimer: I was paid by Google last year to give a short course.
Introduction: That’s the title of my talk at Montana State University this Thursday (21 Mar). For those of you who happen to be in the area, it’s 3:30-5:00pm in the Procrastinator Theater. I’m also speaking in the statistics seminar from 11-12:15 in the Byker Auditorium in the Chemistry building. Topic: Causality and Statistical Learning . P.S. My title is a bit Geng-inspired, which reminds me that I recently came across this interesting mini-bio.
4 0.72306103 407 andrew gelman stats-2010-11-11-Data Visualization vs. Statistical Graphics
Introduction: I have this great talk on the above topic but nowhere to give it. Here’s the story. Several months ago, I was invited to speak at IEEE VisWeek. It sounded like a great opportunity. The organizer told me that there were typically about 700 people in the audience, and these are people in the visualization community whom I’d like to reach but normally wouldn’t have the opportunity to encounter. It sounded great, but I didn’t want to fly most of the way across the country by myself, so I offered to give the talk by videolink. I was surprised to get a No response: I’d think that a visualization conference, of all things, would welcome a video talk. In the meantime, though, I’d thought a lot about what I’d talk about and had started preparing something. Once I found out I wouldn’t be giving the talk, I channeled the efforts into an article which, with the collaboration of Antony Unwin, was completed about a month ago. It would take very little effort to adapt this graph-laden a
5 0.71631354 699 andrew gelman stats-2011-05-06-Another stereotype demolished
Introduction: I’ve heard from various sources that when you give a talk in an econ dept that they eat you alive: typically the audience showers you with questions and you are lucky to get past the second slide in your presentation. So far, though, I’ve given seminar talks in three economics departments–George Mason University a few years ago, Sciences Po last year, and Hunter College yesterday–and all three times the audiences have been completely normal. They did not interrupt unduly and they asked a bunch of good questions at the end. n=3, sure. But still.
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same-blog 1 0.93388969 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 .
2 0.86039454 1542 andrew gelman stats-2012-10-20-A statistical model for underdispersion
Introduction: We have lots of models for overdispersed count data but we rarely see underdispersed data. But now I know what example I’ll be giving when this next comes up in class. From a book review by Theo Tait: A number of shark species go in for oophagy, or uterine cannibalism. Sand tiger foetuses ‘eat each other in utero, acting out the harshest form of sibling rivalry imaginable’. Only two babies emerge, one from each of the mother shark’s uteruses: the survivors have eaten everything else. ‘A female sand tiger gives birth to a baby that’s already a metre long and an experienced killer,’ explains Demian Chapman, an expert on the subject. That’s what I call underdispersion. E(y)=2, var(y)=0. Take that, M. Poisson!
Introduction: That’s ok , Krugman earlier slammed Galbraith. (I wonder if Krugman is as big a fan of “tough choices” now as he was in 1996 .) Given Krugman’s politicization in recent years, I’m surprised he’s so dismissive of the political (rather than technical-economic) nature of Hayek’s influence. (I don’t know if he’s changed his views on Galbraith in recent years.) P.S. Greg Mankiw, in contrast, labels Galbraith and Hayek as “two of the great economists of the 20th century” and writes, “even though their most famous works were written many decades ago, they are still well worth reading today.”
4 0.83969867 1018 andrew gelman stats-2011-11-19-Tempering and modes
Introduction: Gustavo writes: Tempering should always be done in the spirit of *searching* for important modes of the distribution. If we assume that we know where they are, then there is no point to tempering. Now, tempering is actually a *bad* way of searching for important modes, it just happens to be easy to program. As always, my [Gustavo's] prescription is to FIRST find the important modes (as a pre-processing step); THEN sample from each mode independently; and FINALLY weight the samples appropriately, based on the estimated probability mass of each mode, though things might get messy if you end up jumping between modes. My reply: 1. Parallel tempering has always seemed like a great idea, but I have to admit that the only time I tried it (with Matt2 on the tree-ring example), it didn’t work for us. 2. You say you’d rather sample from the modes and then average over them. But that won’t work if if you have a zillion modes. Also, if you know where the modes are, the quickest w
Introduction: Uberbloggers Andrew Sullivan and Matthew Yglesias were kind enough to link to my five-year-old post with graphs from Red State Blue State on time trends of average income by state. Here are the graphs : Yglesias’s take-home point: There isn’t that much change over time in states’ economic well-being. All things considered the best predictor of how rich a state was in 2000 was simply how rich it was in 1929…. Massachusetts and Connecticut have always been rich and Arkansas and Mississippi have always been poor. I’d like to point to a different feature of the graphs, which is that, although the rankings of the states haven’t changed much (as can be seen from the “2000 compared to 1929″ scale), the relative values of the incomes have converged quite a bit—at least, they converged from about 1930 to 1980 before hitting some level of stability. And the rankings have changed a bit. My impression (without checking the numbers) is that New York and Connecticut were
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