andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1912 knowledge-graph by maker-knowledge-mining
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Introduction: Gabriel Murray writes: I saw this post and response from about 5 years ago, regarding a fellow analyzing levels of white blood cells. He was asking about Bayesian approaches to quality control and couldn’t find a canonical resource on that topic. Five years on and I similarly don’t see many good resources on the topic, though it seems like a useful application of Bayesian analysis. Just wondering if you have any further thoughts on the matter now after a few years. In my searching, I did find this interesting book on Bayesian Reliability. It’s not quality control, of course, but it does seem that failure prediction and risk analysis have some bearing on quality control and process control. And the cited reviews are very positive. It was written by some researchers at Los Alamos. Also an edited book on process control: I saw that Bill Bolstad gives it a positive review in one of the journals. And a book on Bayesian risk analysis.
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1 Gabriel Murray writes: I saw this post and response from about 5 years ago, regarding a fellow analyzing levels of white blood cells. [sent-1, score-1.088]
2 He was asking about Bayesian approaches to quality control and couldn’t find a canonical resource on that topic. [sent-2, score-1.315]
3 Five years on and I similarly don’t see many good resources on the topic, though it seems like a useful application of Bayesian analysis. [sent-3, score-0.495]
4 Just wondering if you have any further thoughts on the matter now after a few years. [sent-4, score-0.269]
5 In my searching, I did find this interesting book on Bayesian Reliability. [sent-5, score-0.277]
6 It’s not quality control, of course, but it does seem that failure prediction and risk analysis have some bearing on quality control and process control. [sent-6, score-1.899]
7 It was written by some researchers at Los Alamos. [sent-8, score-0.146]
8 Also an edited book on process control: I saw that Bill Bolstad gives it a positive review in one of the journals. [sent-9, score-0.936]
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same-blog 1 1.0 1912 andrew gelman stats-2013-06-24-Bayesian quality control?
Introduction: Gabriel Murray writes: I saw this post and response from about 5 years ago, regarding a fellow analyzing levels of white blood cells. He was asking about Bayesian approaches to quality control and couldn’t find a canonical resource on that topic. Five years on and I similarly don’t see many good resources on the topic, though it seems like a useful application of Bayesian analysis. Just wondering if you have any further thoughts on the matter now after a few years. In my searching, I did find this interesting book on Bayesian Reliability. It’s not quality control, of course, but it does seem that failure prediction and risk analysis have some bearing on quality control and process control. And the cited reviews are very positive. It was written by some researchers at Los Alamos. Also an edited book on process control: I saw that Bill Bolstad gives it a positive review in one of the journals. And a book on Bayesian risk analysis.
2 0.13492955 1554 andrew gelman stats-2012-10-31-It not necessary that Bayesian methods conform to the likelihood principle
Introduction: Bayesian inference, conditional on the model and data, conforms to the likelihood principle. But there is more to Bayesian methods than Bayesian inference. See chapters 6 and 7 of Bayesian Data Analysis for much discussion of this point. It saddens me to see that people are still confused on this issue.
3 0.13448837 1948 andrew gelman stats-2013-07-21-Bayes related
Introduction: Dave Decker writes: I’ve seen some Bayes related things recently that might make for interesting fodder on your blog. There are two books, teaching Bayesian analysis from a programming perspective. And also a “web application for data analysis using powerful Bayesian statistical methods.” I took a look. The first book is Think Bayes: Bayesian Statistics Made Simple, by Allen B. Downey . It’s super readable and, amazingly, has approximately zero overlap with Bayesian Data Analysis. Downey discusses lots of little problems in a conversational way. In some ways it’s like an old-style math stat textbook (although with a programming rather than mathematical flavor) in that the examples are designed for simplicity rather than realism. I like it! Our book already exists; it’s good to have something else for people to read, coming from an entirely different perspective. The second book is Probabilistic Programming and Bayesian Methods for Hackers , by Cameron Davidson-P
Introduction: Sanjay Kaul writes: I am sure you must be aware of the recent controversy ignited by the 2013 American College of Cardiology/American Heart Association Cholesterol Treatment Guidelines that were released last month. They have been the subject of several newspaper articles and blogs , most of them missing the thrust of the guidelines. There is much to admire about these guidelines as they are more faithfully aligned with high-quality ‘actionable’ evidence than the 3 previous iterations. However, the controversy is focused on the performance of the risk calculator introduced for initiating treatment in individuals without established atherosclerotic disease or diabetes (so-called primary prevention cohort). The guidelines recommend statins for primary prevention in individuals who have a 10-year risk estimated to be 7.5%. The risk calculator was derived from population cohorts studied in the 1990s. The discrimination for predicting the risk of atherosclerotic cardiovascular events
5 0.12710175 1169 andrew gelman stats-2012-02-15-Charles Murray on the new upper class
Introduction: The other day I posted some comments on the voting patterns of rich and poor in the context of Charles Murray’s recent book, “Coming Apart.” My graphs on income and voting are just fine, but I mischaracterized Murray’s statements. So I want to fix that right away. After that I have some thoughts on the book itself. In brief: 1. I was unfair to call him a Tucker Carlson. 2. Murray talks a lot about upper-class liberals. That’s fine but I think his discussion would be improved by also considering upper-class conservatives, given that I see the big culture war occurring within the upper class. 3. Using the case of Joe Paterno as an example, I discuss why Murray’s “preach what you practice” advice could be difficult to carry out in practice. Murray on the top 5% David Frum quoted Murray as writing that the top 5% “tends to be liberal—right? There’s no getting around it. Every way of answering this question produces a yes.” In response, Frum and I both pointed out t
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Introduction: Gabriel Murray writes: I saw this post and response from about 5 years ago, regarding a fellow analyzing levels of white blood cells. He was asking about Bayesian approaches to quality control and couldn’t find a canonical resource on that topic. Five years on and I similarly don’t see many good resources on the topic, though it seems like a useful application of Bayesian analysis. Just wondering if you have any further thoughts on the matter now after a few years. In my searching, I did find this interesting book on Bayesian Reliability. It’s not quality control, of course, but it does seem that failure prediction and risk analysis have some bearing on quality control and process control. And the cited reviews are very positive. It was written by some researchers at Los Alamos. Also an edited book on process control: I saw that Bill Bolstad gives it a positive review in one of the journals. And a book on Bayesian risk analysis.
2 0.76751322 183 andrew gelman stats-2010-08-04-Bayesian models for simultaneous equation systems?
Introduction: A neuroeconomist asks:: Is there any literature on the Bayesian approach to simultaneous equation systems that you could suggest? (Think demand/supply in econ). My reply: I’m not up-to-date on the Bayesian econometrics literature. TTony Lancaster came out with a book a few years ago that might have some of these models. Maybe you, the commenters, have some suggestions? Measurement-error models are inherently Bayesian, seeing as they have all these latent parameters, so it seems like there should be a lot out there.
3 0.73256063 1199 andrew gelman stats-2012-03-05-Any available cookbooks on Bayesian designs?
Introduction: Robert Bell writes: I [Bell] just finished “The Emperor of All Maladies” from 2010. One of the sections is captioned with the “In God we trust, all others bring data” quote which is supposedly from Deming. If you haven’t read it, I think you might like it even though the topic is pretty morbid (i.e. cancer). I was rereading chapter 7 in Bayesian Data Analysis. While the principles there would allow me to “roll my own”, I was wondering whether or not there is now a comprehensive “cookbook” out there that gathers together such analyses for a variety of experimental designs – sort of a Bayesian equivalent to Montgomery’s “Design and Analysis of Experiments”. My reply: I don’t know, but now I’m thinking we should put some of this in our forthcoming book, Statistics for Psychology Research. In the meantime, if anyone has any suggestions, feel free to post them in comments.
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Introduction: Antonio Ramos writes: The book with Hill has very little on longitudinal models. So do you recommended any reference to complement your book on covariance structures typical from these models, such as AR(1), Antedependence, Factor Analytic, etc? I am very much interest in BUGS code for these basic models as well as how to extend them to more complex situations. My reply: There is a book by Banerjee, Carlin, and Gelfand on Bayesian space-time models. Beyond that, I think there is good work in psychometrics on covaraince structures but I don’t know the literature.
5 0.70019352 427 andrew gelman stats-2010-11-23-Bayesian adaptive methods for clinical trials
Introduction: Scott Berry, Brad Carlin, Jack Lee, and Peter Muller recently came out with a book with the above title. The book packs a lot into its 280 pages and is fun to read as well (even if they do use the word “modalities” in their first paragraph, and later on they use the phrase “DIC criterion,” which upsets my tidy, logical mind). The book starts off fast on page 1 and never lets go. Clinical trials are a big part of statistics and it’s cool to see the topic taken seriously and being treated rigorously. (Here I’m not talking about empty mathematical rigor (or, should I say, “rigor”), so-called optimal designs and all that, but rather the rigor of applied statistics, mapping models to reality.) Also I have a few technical suggestions. 1. The authors fit a lot of models in Bugs, which is fine, but they go overboard on the WinBUGS thing. There’s WinBUGS, OpenBUGS, JAGS: they’re all Bugs recommend running Bugs from R using the clunky BRugs interface rather than the smoother bugs(
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Introduction: Gabriel Murray writes: I saw this post and response from about 5 years ago, regarding a fellow analyzing levels of white blood cells. He was asking about Bayesian approaches to quality control and couldn’t find a canonical resource on that topic. Five years on and I similarly don’t see many good resources on the topic, though it seems like a useful application of Bayesian analysis. Just wondering if you have any further thoughts on the matter now after a few years. In my searching, I did find this interesting book on Bayesian Reliability. It’s not quality control, of course, but it does seem that failure prediction and risk analysis have some bearing on quality control and process control. And the cited reviews are very positive. It was written by some researchers at Los Alamos. Also an edited book on process control: I saw that Bill Bolstad gives it a positive review in one of the journals. And a book on Bayesian risk analysis.
2 0.96596229 1824 andrew gelman stats-2013-04-25-Fascinating graphs from facebook data
Introduction: Yair points us to this page full of wonderful graphs from the Stephen Wolfram blog. Here are a few: And some words: People talk less about video games as they get older, and more about politics and the weather. Men typically talk more about sports and technology than women—and, somewhat surprisingly to me, they also talk more about movies, television and music. Women talk more about pets+animals, family+friends, relationships—and, at least after they reach child-bearing years, health. . . . Some of this is rather depressingly stereotypical. And most of it isn’t terribly surprising to anyone who’s known a reasonable diversity of people of different ages. But what to me is remarkable is how we can see everything laid out in such quantitative detail in the pictures above—kind of a signature of people’s thinking as they go through life. Of course, the pictures above are all based on aggregate data, carefully anonymized. But if we start looking at individuals, we’ll s
3 0.96437293 974 andrew gelman stats-2011-10-26-NYC jobs in applied statistics, psychometrics, and causal inference!
Introduction: The Center for the Promotion of Research Involving Innovative Statistical Methodology at the Steinhardt School of Education has two job openings ! One is for an assistant/associated tenure track position for an applied statistician or psychometrician. The other is for a postdoc in causal inference and sensitivity analysis. Jennifer Hill and Marc Scott at the Steinhardt school are just great! We’re working together on various research projects so if you manage to get one of these jobs maybe you can collaborate with us here at Columbia too. So I have every interest in encouraging the very best people to apply for these jobs.
4 0.96434224 246 andrew gelman stats-2010-08-31-Somewhat Bayesian multilevel modeling
Introduction: Eric McGhee writes: I’m trying to generate county-level estimates from a statewide survey of California using multilevel modeling. I would love to learn the full Bayesian approach, but I’m on a tight schedule and worried about teaching myself something of that complexity in the time available. I’m hoping I can use the classical approach and simulate standard errors using what you and Jennifer Hill call the “informal Bayesian” method. This has raised a few questions: First, what are the costs of using this approach as opposed to full Bayesian? Second, when I use the predictive simulation as described on p. 149 of “Data Analysis” on a binary dependent variable and a sample of 2000, I get a 5%-95% range of simulation results so large as to be effectively useless (on the order of +/- 15 points). This is true even for LA county, which has enough cases by itself (about 500) to get a standard error of about 2 points from simple disaggregation. However, if I simulate only with t
Introduction: I pointed Steven Pinker to my post, How much time (if any) should we spend criticizing research that’s fraudulent, crappy, or just plain pointless? , and he responded: Clearly it *is* important to call out publicized research whose conclusions are likely to be false. The only danger is that it’s so easy and fun to criticize, with all the perks of intellectual and moral superiority for so little cost, that there is a moral hazard to go overboard and become a professional slasher and snarker. (That’s a common phenomenon among literary critics, especially in the UK.) There’s also the risk of altering the incentive structure for innovative research, so that researchers stick to the safest kinds of paradigm-twiddling. I think these two considerations were what my late colleague Dan Wegner had in mind when he made the bumbler-pointer contrast — he himself was certainly a discerning critic of social science research. [Just to clarify: Wegner is the person who talked about bumblers and po
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