andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2000 knowledge-graph by maker-knowledge-mining
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Introduction: Joel Greenhouse writes: I saw your recent paper on Feller [see here and, for a more fanciful theory, here ]. Looks like it was fun to write. I recently wrote a paper that asks an orthogonal question to yours. Why during the 1950-1960′s did Jerry Cornfield become a Bayesian? It appeared in Statistics in Medicine – “On becoming a Bayesian: Early correspondences between J. Cornfield and L. J. Savage.” In his paper, Greenhouse writes: Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. . . . Cornfield’s interest in Bayesian methods began prior to 1961 and that the clarity of his Bayesian outlook began to take shape following Birnbaum’s ASA paper on the likelihood prin- cip
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1 Joel Greenhouse writes: I saw your recent paper on Feller [see here and, for a more fanciful theory, here ]. [sent-1, score-0.172]
2 I recently wrote a paper that asks an orthogonal question to yours. [sent-3, score-0.172]
3 It appeared in Statistics in Medicine – “On becoming a Bayesian: Early correspondences between J. [sent-5, score-0.156]
4 ” In his paper, Greenhouse writes: Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. [sent-9, score-0.305]
5 Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. [sent-10, score-0.056]
6 At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. [sent-11, score-0.357]
7 Cornfield’s interest in Bayesian methods began prior to 1961 and that the clarity of his Bayesian outlook began to take shape following Birnbaum’s ASA paper on the likelihood prin- ciple and his subsequent discussions with Savage. [sent-15, score-0.677]
8 This is interesting to me because I find Savage to be the least convincing of early modern Bayesian writers. [sent-16, score-0.142]
9 I always thought Cornfield worked in agricultural experiments, but I guess that was because of his name. [sent-19, score-0.157]
10 Anyway, Joel generously replied: If it makes you feel better during WWII he worked for the Bureau of Labor Statistics and solved a famous a linear programming problem. [sent-21, score-0.292]
11 I’m always glad to hear of others who are interested in the history of statistics. [sent-23, score-0.057]
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Introduction: Joel Greenhouse writes: I saw your recent paper on Feller [see here and, for a more fanciful theory, here ]. Looks like it was fun to write. I recently wrote a paper that asks an orthogonal question to yours. Why during the 1950-1960′s did Jerry Cornfield become a Bayesian? It appeared in Statistics in Medicine – “On becoming a Bayesian: Early correspondences between J. Cornfield and L. J. Savage.” In his paper, Greenhouse writes: Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. . . . Cornfield’s interest in Bayesian methods began prior to 1961 and that the clarity of his Bayesian outlook began to take shape following Birnbaum’s ASA paper on the likelihood prin- cip
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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: Robert Bell pointed me to this post by Brad De Long on Bayesian statistics, and then I also noticed this from Noah Smith, who wrote: My impression is that although the Bayesian/Frequentist debate is interesting and intellectually fun, there’s really not much “there” there… despite being so-hip-right-now, Bayesian is not the Statistical Jesus. I’m happy to see the discussion going in this direction. Twenty-five years ago or so, when I got into this biz, there were some serious anti-Bayesian attitudes floating around in mainstream statistics. Discussions in the journals sometimes devolved into debates of the form, “Bayesians: knaves or fools?”. You’d get all sorts of free-floating skepticism about any prior distribution at all, even while people were accepting without question (and doing theory on) logistic regressions, proportional hazards models, and all sorts of strong strong models. (In the subfield of survey sampling, various prominent researchers would refuse to mode
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Introduction: Joel Greenhouse writes: I saw your recent paper on Feller [see here and, for a more fanciful theory, here ]. Looks like it was fun to write. I recently wrote a paper that asks an orthogonal question to yours. Why during the 1950-1960′s did Jerry Cornfield become a Bayesian? It appeared in Statistics in Medicine – “On becoming a Bayesian: Early correspondences between J. Cornfield and L. J. Savage.” In his paper, Greenhouse writes: Jerome Cornfield was arguably the leading proponent for the use of Bayesian methods in biostatistics during the 1960s. Prior to 1963, however, Cornfield had no publications in the area of Bayesian statistics. At a time when frequentist methods were the dominant influence on statistical practice, Cornfield went against the mainstream and embraced Bayes. . . . Cornfield’s interest in Bayesian methods began prior to 1961 and that the clarity of his Bayesian outlook began to take shape following Birnbaum’s ASA paper on the likelihood prin- cip
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Introduction: Updated version of my paper with Xian: The missionary zeal of many Bayesians of old has been matched, in the other direction, by an attitude among some theoreticians that Bayesian methods are absurd—not merely misguided but obviously wrong in principle. We consider several examples, beginning with Feller’s classic text on probability theory and continuing with more recent cases such as the perceived Bayesian nature of the so-called doomsday argument. We analyze in this note the intellectual background behind various misconceptions about Bayesian statistics, without aiming at a complete historical coverage of the reasons for this dismissal. I love this stuff.
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Introduction: Alexander at GiveWell writes : The Disease Control Priorities in Developing Countries (DCP2), a major report funded by the Gates Foundation . . . provides an estimate of $3.41 per disability-adjusted life-year (DALY) for the cost-effectiveness of soil-transmitted-helminth (STH) treatment, implying that STH treatment is one of the most cost-effective interventions for global health. In investigating this figure, we have corresponded, over a period of months, with six scholars who had been directly or indirectly involved in the production of the estimate. Eventually, we were able to obtain the spreadsheet that was used to generate the $3.41/DALY estimate. That spreadsheet contains five separate errors that, when corrected, shift the estimated cost effectiveness of deworming from $3.41 to $326.43. [I think they mean to say $300 -- ed.] We came to this conclusion a year after learning that the DCP2’s published cost-effectiveness estimate for schistosomiasis treatment – another kind of
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Introduction: I was trying to make some new graphs using 5-year-old R code and I got all these problems because I was reading in files with variable names such as “co.fipsid” and now R is automatically changing them to “co_fipsid”. Or maybe the names had underbars all along, and the old R had changed them into dots. Whatever. I understand that backward compatibility can be hard to maintain, but this is just annoying.
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Introduction: Troels Ring writes: I have measured total phosphorus, TP, on a number of dialysis patients, and also measured conventional phosphate, Pi. Now P is exchanged with the environment as Pi, so in principle a correlation between TP and Pi could perhaps be expected. I’m really most interested in the fraction of TP which is not Pi, that is TP-Pi. I would also expect that to be positively correlated with Pi. However, looking at the data using a mixed model an insignificant negative correlation is obtained. Then I thought, that since TP-Pi is bound to be small if Pi is large a negative correlation is almost dictated by the math even if the biology would have it otherwise in so far as the the TP-Pi, likely organic P, must someday have been Pi. Hence I thought about correcting the slight negative correlation between TP-Pi and Pi for the expected large negative correlation due to the math – to eventually recover what I came from: a positive correlation. People seems to agree that this thinki
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Introduction: I received the following (unsolicited) email: Hello, *** LLC, a ***-based market research company, has a financial client who is interested in speaking with a statistician who has done research in the field of Alzheimer’s Disease and preferably familiar with the SOLA and BAPI trials. We offer an honorarium of $200 for a 30 minute telephone interview. Please advise us if you have an employment or consulting agreement with any organization or operate professionally pursuant to an organization’s code of conduct or employee manual that may control activities by you outside of your regular present and former employment, such as participating in this consulting project for MedPanel. If there are such contracts or other documents that do apply to you, please forward MedPanel a copy of each such document asap as we are obligated to review such documents to determine if you are permitted to participate as a consultant for MedPanel on a project with this particular client. If you are
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