andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-869 knowledge-graph by maker-knowledge-mining
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Introduction: Maurizio Pisati sends along this presentation of work with Valeria Glorioso. He writes: “Our major problem, now, is uncertainty estimation — we’re still struggling to find a solution appropriate to the Stata environment.”
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same-blog 1 0.99999994 869 andrew gelman stats-2011-08-24-Mister P in Stata
Introduction: Maurizio Pisati sends along this presentation of work with Valeria Glorioso. He writes: “Our major problem, now, is uncertainty estimation — we’re still struggling to find a solution appropriate to the Stata environment.”
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Introduction: Xian sends along this link that might be of interest to some of you.
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Introduction: A student I’m working with writes: I was planning on getting a applied stat text as a desk reference, and for that I’m assuming you’d recommend your own book. Also, being an economics student, I was initially planning on doing my analysis in STATA, but I noticed on your blog that you use R, and apparently so does the rest of the statistics profession. Would you rather I do my programming in R this summer, or does it not matter? It doesn’t look too hard to learn, so just let me know what’s most convenient for you. My reply: Yes, I recommend my book with Jennifer Hill. Also the book by John Fox, An R and S-plus Companion to Applied Regression, is a good way to get into R. I recommend you use both Stata and R. If you’re already familiar with Stata, then stick with it–it’s a great system for working with big datasets. You can grab your data in Stata, do some basic manipulations, then save a smaller dataset to read into R (using R’s read.dta() function). Once you want to make fu
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Introduction: Expectation propagation and approximate Bayesian computation. Here are X’s comments on a paper, “Expectation-Propagation for Likelihood-Free Inference,” by Simon Barthelme and Nicolas Chopin. The paper is not new but the topic is still hot. Also there’s this paper by Maurizio Filippone and Mark Girolami on computation for Gaussian process models. I wonder how this connects to GPstuff , which I think is what Aki did to fit the birthdays model: This stuff is where it’s at.
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Introduction: Is there an implementation of bayesglm in Stata? (That is, approximate maximum penalized likelihood estimation with specified normal or t prior distributions on the coefficients.)
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Introduction: Maurizio Pisati sends along this presentation of work with Valeria Glorioso. He writes: “Our major problem, now, is uncertainty estimation — we’re still struggling to find a solution appropriate to the Stata environment.”
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Introduction: John Transue sends along a link to this software for extracting data from graphs. I haven’t tried it out but it could be useful to somebody out there?
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Introduction: Xian sends along this link that might be of interest to some of you.
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Introduction: Mike Betancourt sends along this paper . Could be interesting, no? Note the heavy tail on the CDF in Figure 3, exhibiting weakened median time since 1999. And, as you can see from the bibliography, the work draws on a variety of sources:
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Introduction: Lee Wilkinson sends me this amusing ad for his new software, AdviseStat: The ad is a parody, but the software is real !
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Introduction: Maurizio Pisati sends along this presentation of work with Valeria Glorioso. He writes: “Our major problem, now, is uncertainty estimation — we’re still struggling to find a solution appropriate to the Stata environment.”
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