andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2277 knowledge-graph by maker-knowledge-mining
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Introduction: Robert Grant has a list . I’ll just give the ones with more than 10,000 Google Scholar cites: Cox (1972) Regression and life tables: 35,512 citations. Dempster, Laird, Rubin (1977) Maximum likelihood from incomplete data via the EM algorithm: 34,988 Bland & Altman (1986) Statistical methods for assessing agreement between two methods of clinical measurement: 27,181 Geman & Geman (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images: 15,106 We can find some more via searching Google scholar for familiar names and topics; thus: Metropolis et al. (1953) Equation of state calculations by fast computing machines: 26,000 Benjamini and Hochberg (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing: 21,000 White (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity: 18,000 Heckman (1977) Sample selection bias as a specification error:
sentIndex sentText sentNum sentScore
1 But I’m guessing there are some biggies I’m missing. [sent-5, score-0.084]
2 I say this because Grant’s original list included one paper, by Bland and Altman, with over 27,000 cites, that I’d never heard of! [sent-6, score-0.133]
3 I agree with Grant that using Google Scholar favors newer papers. [sent-9, score-0.068]
4 For example, Cooley and Tukey (1965), “An algorithm for the machine calculation of complex Fourier series,” does not make the list, amazingly enough, with only 9300 cites. [sent-10, score-0.167]
5 And the hugely influential book by Snedecor and Cochran has very few cites, I guess cos nobody cites it anymore. [sent-11, score-0.419]
6 And, of course, the most influential researchers such as Laplace, Gauss, Fisher, Neyman, Pearson, etc. [sent-12, score-0.104]
7 If Pearson got a cite for every chi-squared test, Neyman for every rejection region, Fisher for every maximum-likelihood estimate, etc. [sent-14, score-0.234]
8 , their citations would run into the mid to high zillions each. [sent-15, score-0.197]
9 I wrote this post a few months ago so all the citations have gone up. [sent-19, score-0.113]
10 For example, the fuzzy sets paper is now listed at 49,000, and Zadeh has a second paper, “Outline of a new approach to the analysis of complex systems and decision processes,” with 16,000 cites. [sent-20, score-0.385]
11 On the upside, Efron’s 1979 paper, “Bootstrap methods: another look at the jackknife,” has just pulled itself over the 10,000 cites mark. [sent-22, score-0.315]
12 Also, I just checked and Tibshirani’s paper on lasso is at 9873, so in the not too distant future it will make the list too. [sent-24, score-0.221]
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same-blog 1 1.0 2277 andrew gelman stats-2014-03-31-The most-cited statistics papers ever
Introduction: Robert Grant has a list . I’ll just give the ones with more than 10,000 Google Scholar cites: Cox (1972) Regression and life tables: 35,512 citations. Dempster, Laird, Rubin (1977) Maximum likelihood from incomplete data via the EM algorithm: 34,988 Bland & Altman (1986) Statistical methods for assessing agreement between two methods of clinical measurement: 27,181 Geman & Geman (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images: 15,106 We can find some more via searching Google scholar for familiar names and topics; thus: Metropolis et al. (1953) Equation of state calculations by fast computing machines: 26,000 Benjamini and Hochberg (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing: 21,000 White (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity: 18,000 Heckman (1977) Sample selection bias as a specification error:
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Introduction: Christian Robert is planning a graduate seminar in which students read 15 classic articles of statistics. (See here for more details and a slightly different list.) Actually, he just writes “classics,” but based on his list, I assume he only wants articles, not books. If he wanted to include classic books, I’d nominate the following, just for starters: - Fisher’s Statistical Methods for Research Workers - Snedecor and Cochran’s Statistical Methods - Kish’s Survey Sampling - Box, Hunter, and Hunter’s Statistics for Experimenters - Tukey’s Exploratory Data Analysis - Cleveland’s The Elements of Graphing Data - Mosteller and Wallace’s book on the Federalist Papers. Probably Cox and Hinkley, too. That’s a book that I don’t think has aged well, but it seems to have had a big influence. I think there’s a lot more good and accessible material in these classic books than in the equivalent volume of classic articles. Journal articles can be difficult to read and are typicall
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Introduction: Mark Palko asks what I think of this article by Francisco Louca, who writes about “‘hybridization’, a synthesis between Fisherian and Neyman-Pearsonian precepts, defined as a number of practical proceedings for statistical testing and inference that were developed notwithstanding the original authors, as an eventual convergence between what they considered to be radically irreconcilable.” To me, the statistical ideas in this paper are too old-fashioned. The issue is not that the Neyman-Pearson and Fisher approaches are “irreconcilable” but rather that neither does the job in the sort of hard problems that face statistical science today. I’m thinking of technically difficult models such as hierarchical Gaussian processes and also challenges that arise with small sample size and multiple testing. Neyman, Pearson, and Fisher all were brilliant, and they all developed statistical methods that remain useful today, but I think their foundations are out of date. Yes, we currently use m
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Introduction: Robert Grant has a list . I’ll just give the ones with more than 10,000 Google Scholar cites: Cox (1972) Regression and life tables: 35,512 citations. Dempster, Laird, Rubin (1977) Maximum likelihood from incomplete data via the EM algorithm: 34,988 Bland & Altman (1986) Statistical methods for assessing agreement between two methods of clinical measurement: 27,181 Geman & Geman (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images: 15,106 We can find some more via searching Google scholar for familiar names and topics; thus: Metropolis et al. (1953) Equation of state calculations by fast computing machines: 26,000 Benjamini and Hochberg (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing: 21,000 White (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity: 18,000 Heckman (1977) Sample selection bias as a specification error:
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Introduction: I’m on an island in Maine for a few weeks (big shout out for North Haven!) This morning I picked up a copy of “Working Waterfront,” a newspaper that focuses on issues of coastal fishing communities. I came across an article about modeling “fish” populations — actually lobsters, I guess they’re considered “fish” for regulatory purposes. When I read it, I thought “wow, this article is really well-written, not dumbed down like articles in most newspapers.” I think it’s great that a small coastal newspaper carries reporting like this. (The online version has a few things that I don’t recall in the print version, too, so it’s even better). But in addition to being struck by finding such a good article in a small newspaper, I was struck by this: According to [University of Maine scientist Yong] Chen, there are four main areas where his model improved on the prior version. “We included the inshore trawl data from Maine and other state surveys, in addition to federal survey data; we h
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