andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2273 knowledge-graph by maker-knowledge-mining
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Introduction: A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could provide data w/ R and/or BUGS codes that I could replicate them? My reply: For Bayesian hierarchical (multi-level) linear regression and causal inference, see my book with Jennifer Hill. For Bayesian structural equation modeling, try google and you’ll find some good stuff. Also, I recommend Stan (http://mc-stan.org/) rather than Bugs.
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1 A student writes: I am new to Bayesian methods. [sent-1, score-0.154]
2 While I am reading your book, I have some questions for you. [sent-2, score-0.173]
3 I am interested in doing Bayesian hierarchical (multi-level) linear regression (e. [sent-3, score-0.707]
4 , random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. [sent-5, score-0.875]
5 Do you happen to know if I could find some articles, where authors could provide data w/ R and/or BUGS codes that I could replicate them? [sent-6, score-1.246]
6 My reply: For Bayesian hierarchical (multi-level) linear regression and causal inference, see my book with Jennifer Hill. [sent-7, score-0.908]
7 For Bayesian structural equation modeling, try google and you’ll find some good stuff. [sent-8, score-1.059]
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Introduction: A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could provide data w/ R and/or BUGS codes that I could replicate them? My reply: For Bayesian hierarchical (multi-level) linear regression and causal inference, see my book with Jennifer Hill. For Bayesian structural equation modeling, try google and you’ll find some good stuff. Also, I recommend Stan (http://mc-stan.org/) rather than Bugs.
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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.
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Introduction: Ban Chuan Cheah writes: In a previous post, http://andrewgelman.com/2013/07/30/the-roy-causal-model/ you pointed to a paper on Bayesian methods by Heckman. At around the same time I came across another one of his papers, “The Effects of Cognitive and Noncognitive Abilities on Labor Market Outcomes and Social Behavior (2006)” (http://www.nber.org/papers/w12006 or published version http://www.jstor.org/stable/10.1086/504455). In this paper they implement their model as follows: We use Bayesian Markov chain Monte Carlo methods to compute the sample likelihood. Our use of Bayesian methods is only a computational convenience. Our identification analysis is strictly classical. Under our assumptions, the priors we use are asymptotically irrelevant. Some of the authors have also done something similar earlier in: Hansen, Karsten T. & Heckman, James J. & Mullen, K.J.Kathleen J., 2004. “The effect of schooling and ability on achievement test scores,” Journal of Econometrics, Elsevi
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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.
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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: A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could provide data w/ R and/or BUGS codes that I could replicate them? My reply: For Bayesian hierarchical (multi-level) linear regression and causal inference, see my book with Jennifer Hill. For Bayesian structural equation modeling, try google and you’ll find some good stuff. Also, I recommend Stan (http://mc-stan.org/) rather than Bugs.
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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
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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: Charlie Williams writes: As I get interested in Bayesian approaches to statistics, I have one question I wondered if you would find interesting to address at some point on the blog. What does Bayesian work look like in action across a field? From experience, I have some feeling for how ongoing debates evolve (or not) with subsequent studies in response to earlier findings. I wonder if you know how this happens in practice when multiple researchers are using Bayesian approaches. How much are previous findings built into priors? How much advance comes from model improvement? And in a social science field where self-selection and self-interest play a role, how are improved “treatment” effects incorporated and evaluated? I thought you might know of a field where actual back and forth has been carried out mostly in the context of Bayesian analysis or inference, and I thought it would be interesting to take a look at an example as I think about my own field. My reply: I’ve seen Ba
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Introduction: A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could provide data w/ R and/or BUGS codes that I could replicate them? My reply: For Bayesian hierarchical (multi-level) linear regression and causal inference, see my book with Jennifer Hill. For Bayesian structural equation modeling, try google and you’ll find some good stuff. Also, I recommend Stan (http://mc-stan.org/) rather than Bugs.
2 0.96159321 2127 andrew gelman stats-2013-12-08-The never-ending (and often productive) race between theory and practice
Introduction: Commenter Wonks Anonymous writes : After the recent EconNobel announcement I decided to check Dimensional’s Fama-French blog to see if it had much new content recently, and while it was dissapointingly sparse it did have an interesting bit where Fama linked to the best advice he’d ever gotten , from his statistics professor Harry Roberts: With formal statistics, you say something — a hypothesis — and then you test it. Harry always said that your criterion should be not whether or not you can reject or accept the hypothesis, but what you can learn from the data. The best thing you can do is use the data to enhance your description of the world. I responded: That’s a great quote. Except that I disagree with what Fama says about “formal statistics.” Or, should I say, he has an old-fashioned view of formal statistics. (See this paper by X and me for some discussion of old-fashioned views.) Nowadays, lots of formal statistics is all about what you can learn from the data, no
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Introduction: Bill Harris writes: I’ve read your paper and presentation showing why you don’t usually worry about multiple comparisons. I see how that applies when you are comparing results across multiple settings (states, etc.). Does the same principle hold when you are exploring data to find interesting relationships? For example, you have some data, and you’re trying a series of models to see which gives you the most useful insight. Do you try your models on a subset of the data so you have another subset for confirmatory analysis later, or do you simply throw all the data against your models? My reply: I’d like to estimate all the relationships at once and use a multilevel model to do partial pooling to handle the mutiplicity issues. That said, in practice, in my applied work I’m always bouncing back and forth between different hypotheses and different datasets, and often I learn a lot when next year’s data come in and I can modify my hypotheses. The trouble with the classical
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Introduction: Chris Harrison writes: I have just come across your paper in the 2009 American Scientist. Another problem that I frequently come across is when people do power spectral analyses of signals. If one has 1200 points (fairly modest in this day and age) then there are 600 power spectral estimates. People will then determine the 95% confidence limits and pick out any spectral estimate that sticks up above this, claiming that it is significant. But there will be on average 30 estimates that stick up too high or too low. So in general there will be 15 spectral estimates which are higher than the 95% confidence limit which could happen just by chance. I suppose that this means that you have to set a much higher confidence limit, which would depend on the number of data in your signal. I would also like your opinion about a paper in the Proceedings of the National Academy of Science, “The causality analysis of climate change and large-scale human crisis” by David D. Zhang, Harry F. L
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