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442 andrew gelman stats-2010-12-01-bayesglm in Stata?


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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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1 (That is, approximate maximum penalized likelihood estimation with specified normal or t prior distributions on the coefficients. [sent-2, score-2.344]


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Introduction: Maximum likelihood gives the beat fit to the training data but in general overfits, yielding overly-noisy parameter estimates that don’t perform so well when predicting new data. A popular solution to this overfitting problem takes advantage of the iterative nature of most maximum likelihood algorithms by stopping early. In general, an iterative optimization algorithm goes from a starting point to the maximum of some objective function. If the starting point has some good properties, then early stopping can work well, keeping some of the virtues of the starting point while respecting the data. This trick can be performed the other way, too, starting with the data and then processing it to move it toward a model. That’s how the iterative proportional fitting algorithm of Deming and Stephan (1940) works to fit multivariate categorical data to known margins. In any case, the trick is to stop at the right point–not so soon that you’re ignoring the data but not so late that you en

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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: 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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