andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1188 knowledge-graph by maker-knowledge-mining
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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.
sentIndex sentText sentNum sentScore
1 Antonio Ramos writes: The book with Hill has very little on longitudinal models. [sent-1, score-0.487]
2 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? [sent-2, score-1.455]
3 I am very much interest in BUGS code for these basic models as well as how to extend them to more complex situations. [sent-3, score-0.873]
4 My reply: There is a book by Banerjee, Carlin, and Gelfand on Bayesian space-time models. [sent-4, score-0.213]
5 Beyond that, I think there is good work in psychometrics on covaraince structures but I don’t know the literature. [sent-5, score-0.734]
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same-blog 1 0.99999994 1188 andrew gelman stats-2012-02-28-Reference on longitudinal models?
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.
Introduction: Devrup Ghatak writes: I am a student of economics and recently read your review of Mostly Harmless Econometrics. In the review you mention that the book contains no time series. Given that your book on data analysis (Data Analysis using Regression) does not contain any time series material either, I wonder if you happen to have any favourite time series reference similar in style/level to the data analysis book. I don’t know. The closest thing might be Hierarchical Modeling and Analysis for Spatial Data by Banerjee, Carlin, and Gelfand, but I don’t know of anything focused on time series that’s quite in the format that I’d prefer. This is not my area, though. Maybe you, the readers, have some suggestions?
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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: In an article catchily entitled, “I got more data, my model is more refined, but my estimator is getting worse! Am I just dumb?”, Meng and Xie write: Possibly, but more likely you are merely a victim of conventional wisdom. More data or better models by no means guarantee better estimators (e.g., with a smaller mean squared error), when you are not following probabilistically principled methods such as MLE (for large samples) or Bayesian approaches. Estimating equations are par- ticularly vulnerable in this regard, almost a necessary price for their robustness. These points will be demonstrated via common tasks of estimating regression parameters and correlations, under simple mod- els such as bivariate normal and ARCH(1). Some general strategies for detecting and avoiding such pitfalls are suggested, including checking for self-efficiency (Meng, 1994, Statistical Science) and adopting a guiding working model. Using the example of estimating the autocorrelation ρ under a statio
Introduction: David Duvenaud writes: I’ve been following your recent discussions about how an AI could do statistics [see also here ]. I was especially excited about your suggestion for new statistical methods using “a language-like approach to recursively creating new models from a specified list of distributions and transformations, and an automatic approach to checking model fit.” Your discussion of these ideas was exciting to me and my colleagues because we recently did some work taking a step in this direction, automatically searching through a grammar over Gaussian process regression models. Roger Grosse previously did the same thing , but over matrix decomposition models using held-out predictive likelihood to check model fit. These are both examples of automatic Bayesian model-building by a search over more and more complex models, as you suggested. One nice thing is that both grammars include lots of standard models for free, and they seem to work pretty well, although the
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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.
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Introduction: Ben points us to a new book, Flexible Imputation of Missing Data . It’s excellent and I highly recommend it. Definitely worth the $89.95. Van Buuren’s book is great even if you don’t end up using the algorithm described in the book (I actually like their approach but I do think there are some limitations with their particular implementation, which is one reason we’re developing our own package ); he supplies lots of intuition, examples, and graphs. P.S. Stef’s book features an introduction by Don Rubin, which gets me thinking: if Don can find the time to write an introduction to somebody else’s book, he surely should be willing to read and comment on the third edition of his own book, no?
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Introduction: Joshua Vogelstein writes: Are you aware of a paper the explains current best practice of figure generation, in general? i’m thinking things like: have legends and labels that are legible, etc. seems like you or hadley shoulda written some such thing by now…. My reply: A couple of sources I can think of are: one of the appendixes in my book with Jennifer, and the book by Rafe Donahue.
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Introduction: Eric Novik does some open-source planning : My co-author, Jacki Buros, and I [Novik] have just signed a contract with Apress to write a book tentatively entitled “Predictive Analytics with R”, which will cover programming best practices, data munging, data exploration, and single and multi-level models with case studies in social media, healthcare, politics, marketing, and the stock market. Why does the world need another R book? We think there is a shortage of books that deal with the complete and programmer centric analysis of real, dirty, and sometimes unstructured data. Our target audience are people who have some familiarity with statistics, but do not have much experience with programming. . . . The book is projected to be about 300 pages across 8 chapters. This is my first experience with writing a book and everything I heard about the process tells me that this is going to be a long and arduous endeavor lasting anywhere from 6 to 8 months. Novik emailed me and wrot
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Introduction: Willard Cope Brinton’s second book Graphic Presentation (1939) surprised me with the quality of its graphics. Prof. Michael Stoll has some scans at Flickr . For example: The whole book can be downloaded (in a worse resolution) from Archive.Org .
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1 0.90496814 41 andrew gelman stats-2010-05-19-Updated R code and data for ARM
Introduction: Patricia and I have cleaned up some of the R and Bugs code and collected the data for almost all the examples in ARM. See here for links to zip files with the code and data.
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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.
3 0.81029224 179 andrew gelman stats-2010-08-03-An Olympic size swimming pool full of lithium water
Introduction: As part of his continuing plan to sap etc etc., Aleks pointed me to an article by Max Miller reporting on a recommendation from Jacob Appel: Adding trace amounts of lithium to the drinking water could limit suicides. . . . Communities with higher than average amounts of lithium in their drinking water had significantly lower suicide rates than communities with lower levels. Regions of Texas with lower lithium concentrations had an average suicide rate of 14.2 per 100,000 people, whereas those areas with naturally higher lithium levels had a dramatically lower suicide rate of 8.7 per 100,000. The highest levels in Texas (150 micrograms of lithium per liter of water) are only a thousandth of the minimum pharmaceutical dose, and have no known deleterious effects. I don’t know anything about this and am offering no judgment on it; I’m just passing it on. The research studies are here and here . I am skeptical, though, about this part of the argument: We are not talking a
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Introduction: I opened the newspaper today (recall that this blog is on an approximately one-month delay) to see a moderately horrifying story about art appraisers who are deterred by fear of lawsuits from expressing an opinion about possible forgeries. Maybe this trend will come to science too? Perhaps Brett Pelham will sue Uri Simonsohn for the pain, suffering, and loss of income occurring from the questioning of his Dennis the dentist paper ? Or maybe I’ll be sued by some rogue sociologist for publicly questioning his data dredging? Anyway, what amused me about the NYT article on art forgery was that two of the artists featured in the discussion were . . . Andy Warhol and Roy Lichtenstein! Warhol is famous for diluting the notion of the unique art object and for making works of art in a “Factory,” and Lichtenstein is famous for ripping off the style and imagery of comic book artists. It’s funny for the two of them, of all people, to come up in a discussion of authenticity. Or maybe it
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Introduction: Hykel Hosni noticed this bit from the Lindley Prize page of the Society for Bayesan Analysis: Lindley became a great missionary for the Bayesian gospel. The atmosphere of the Bayesian revival is captured in a comment by Rivett on Lindley’s move to University College London and the premier chair of statistics in Britain: “it was as though a Jehovah’s Witness had been elected Pope.” From my perspective, this was amusing (if commonplace): a group of rationalists jocularly characterizing themselves as religious fanatics. And some of this is in response to intense opposition from outsiders (see the Background section here ). That’s my view. I’m an insider, a statistician who’s heard all jokes about religious Bayesians, from Bayesian and non-Bayesian statisticians alike. But Hosni is an outsider, and here’s how he sees the above-quoted paragraph: Research, however, is not a matter of faith but a matter of arguments, which should always be evaluated with the utmost intellec
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