andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1931 knowledge-graph by maker-knowledge-mining

1931 andrew gelman stats-2013-07-09-“Frontiers in Massive Data Analysis”


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Introduction: Mike Jordan sends along this National Academies report on “big data.” This is not a research report but it could be interesting in that it conveys what are believed to be important technical challenges.


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1 Mike Jordan sends along this National Academies report on “big data. [sent-1, score-0.716]

2 ” This is not a research report but it could be interesting in that it conveys what are believed to be important technical challenges. [sent-2, score-1.566]


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Introduction: Mike Jordan sends along this National Academies report on “big data.” This is not a research report but it could be interesting in that it conveys what are believed to be important technical challenges.

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Introduction: Follow the discussion (originated by Mike Jordan) at the Statistics Forum.

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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:

5 0.13008526 1725 andrew gelman stats-2013-02-17-“1.7%” ha ha ha

Introduction: Jordan Ellenberg writes: Lots of people sharing this today. Isn’t this exactly the kind of situation where they should have done some kind of shrinkage towards the national mean, as in that thing you wrote about kidney cancer rates by county? i.e. you see, just as you might expect, the extreme values of “proportion of people who said they were gay” are disproportionately taken by small states. My reply: If I don’t have the individual-level survey data that would allow me to do full-scale Mister P , yes, I’d fit a multilevel model to the state-level averages. I wouldn’t quite just partially pool toward the national mean; I think it would make sense to include some state-level predictors. In any case, I think it’s tacky to report poll numbers to fractional percentage points. That kind of precision simply isn’t there. P.S. More discussion of variances of large and small states in the comments .

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Introduction: Mike Spagat sends along a serious presentation with an ironic title: 18.7 MILLION ANNIHILATED SAYS LEADING EXPERT IN PEER–REVIEWED JOURNAL: AN APPROVED, AUTHORITATIVE, SCIENTIFIC PRESENTATION MADE BY AN EXPERT He’ll be speaking on it at tomorrow’s meeting of the Catastrophes and Conflict Forum of the Royal Society of Medicine in London. All I can say is, it’s a long time since I’ve seen a slide presentation in portrait form. It brings me back to the days of transparency sheets.

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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: Matthew Yglesias discusses how West Virginia used to be a Democratic state but is now solidly Republican. I thought it would be helpful to expand this to look at trends since 1948 (rather than just 1988) and all 50 states (rather than just one). This would represent a bit of work, except that I already did it a couple years ago, so here it is (right-click on the image to see the whole thing): I cheated a bit to get reasonable-looking groupings, for example putting Indiana in the Border South rather than Midwest, and putting Alaska in Mountain West and Hawaii in West Coast. Also, it would help to distinguish states by color (to be able to disentangle New Jersey and Delaware, for example) but we didn’t do this because the book is mostly black and white. In any case, the picture makes it clear that there have been strong regional trends all over during the past sixty years. P.S. My graph comes from Red State Blue State so no 2008 data, but 2008 was pretty much a shift

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