andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2283 knowledge-graph by maker-knowledge-mining
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Introduction: I happened to be reading an old comment thread from 2012 (follow the link from here ) and came across this amusing exchange: Perhaps this is the paper Jonathan was talking about? Here’s more from the thread: Anyway, I don’t have anything to add right now, I just thought it was an interesting discussion.
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1 I happened to be reading an old comment thread from 2012 (follow the link from here ) and came across this amusing exchange: Perhaps this is the paper Jonathan was talking about? [sent-1, score-2.303]
2 Here’s more from the thread: Anyway, I don’t have anything to add right now, I just thought it was an interesting discussion. [sent-2, score-0.663]
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same-blog 1 1.0 2283 andrew gelman stats-2014-04-06-An old discussion of food deserts
Introduction: I happened to be reading an old comment thread from 2012 (follow the link from here ) and came across this amusing exchange: Perhaps this is the paper Jonathan was talking about? Here’s more from the thread: Anyway, I don’t have anything to add right now, I just thought it was an interesting discussion.
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Introduction: Amusing authorship analysis.
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Introduction: Those of you who know what I’m talking about, know what I’m talking about.
Introduction: We were having so much fun on this thread that I couldn’t resist linking to this news item by Adrian Chen. The good news is that Scott Adams (creater of the Dilbert comic strip) “has a certified genius IQ” and that he “can open jars with [his] bare hands.” He is also “able to lift heavy objects.” Cool! In all seriousness, I knew nothing about this aspect of Adams when I wrote the earlier blog. I was just surprised (and remain surprised) that he was so impressed with Charlie Sheen for being good-looking and being able to remember his lines. At the time I thought it was just a matter of Adams being overly-influenced by his direct experience, along with some satisfaction in separating himself from the general mass of Sheen-haters out there. But now I wonder if something more is going on, that maybe he feels that he and Sheen are on the same side in a culture war. In any case, the ultimate topic of interest here is not Sheen or Adams but rather more general questions of what
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Introduction: Upon reading this , Susan remarked, “Don’t you think it’s interesting that a guy who promotes smoking has a last name of ‘Huff’? Reminds me of the Dennis/Dentist studies.” Good point. P.S. As discussed in the linked thread, the great statistician R. A. Fisher was notorious for minimizing the risks of smoking. How does this connect to Fisher’s name, one might ask?
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same-blog 1 0.9905327 2283 andrew gelman stats-2014-04-06-An old discussion of food deserts
Introduction: I happened to be reading an old comment thread from 2012 (follow the link from here ) and came across this amusing exchange: Perhaps this is the paper Jonathan was talking about? Here’s more from the thread: Anyway, I don’t have anything to add right now, I just thought it was an interesting discussion.
2 0.80221528 1433 andrew gelman stats-2012-07-28-LOL without the CATS
Introduction: Mayo points me to this discussion [link fixed] on parsimony by philosopher Elliott Sober. I don’t really understand what he’s talking about but I am posting the link here because it might interest some of you. P.S. More discussion on this from Mayo here .
3 0.72725254 953 andrew gelman stats-2011-10-11-Steve Jobs’s cancer and science-based medicine
Introduction: Interesting discussion from David Gorski (which I found via this link from Joseph Delaney). I don’t have anything really to add to this discussion except to note the value of this sort of anecdote in a statistics discussion. It’s only n=1 and adds almost nothing to the literature on the effectiveness of various treatments, but a story like this can help focus one’s thoughts on the decision problems.
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Introduction: The Browser asked me to recommend 6 articles for their readers. Here’s what I came up with. I really wanted to link to this one but it wouldn’t mean much to people who don’t know New York. I also recommended this (if you’ll forgive my reference to bowling), but I think it was too much of a primary source for their taste.
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Introduction: Oof! (if you’ll forgive my reference to bowling) What’s funny to me, though, is the phrase, “she’s not nearly as smart as she seems to think she is.” I mean, doesn’t that describe most people? (Link from here .) P.S. I hate to spell things out, Jeff, but . . . I hope you caught the Douglas Ginsburg reference!
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same-blog 1 0.99677676 2283 andrew gelman stats-2014-04-06-An old discussion of food deserts
Introduction: I happened to be reading an old comment thread from 2012 (follow the link from here ) and came across this amusing exchange: Perhaps this is the paper Jonathan was talking about? Here’s more from the thread: Anyway, I don’t have anything to add right now, I just thought it was an interesting discussion.
Introduction: From 2.5 years ago . Read all the comments; the discussion is helpful.
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Introduction: Joe Fruehwald writes: I’m working with linguistic data, specifically binomial hits and misses of a certain variable for certain words (specifically whether or not the “t” sound was pronounced at the end of words like “soft”). Word frequency follows a power law, with most words appearing just once, and with some words being hyperfrequent. I’m not interested in specific word effects, but I am interested in the effect of word frequency. A logistic model fit is going to be heavily influenced by the effect of the hyperfrequent words which constitute only one type. To control for the item effect, I would fit a multilevel model with a random intercept by word, but like I said, most of the words appear only once. Is there a principled approach to this problem? My response: It’s ok to fit a multilevel model even if most groups only have one observation each. You’ll want to throw in some word-level predictors too. Think of the multilevel model not as a substitute for the usual thoug
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Introduction: If being cantankerous and potty-mouthed is a bad thing, I’m in big trouble !
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Introduction: John Lawson writes: I have been experimenting using Bayesian Methods to estimate variance components, and I have noticed that even when I use a noninformative prior, my estimates are never close to the method of moments or REML estimates. In every case I have tried, the sum of the Bayesian estimated variance components is always larger than the sum of the estimates obtained by method of moments or REML. For data sets I have used that arise from a simple one-way random effects model, the Bayesian estimates of the between groups variance component is usually larger than the method of moments or REML estimates. When I use a uniform prior on the between standard deviation (as you recommended in your 2006 paper ) rather than an inverse gamma prior on the between variance component, the between variance component is usually reduced. However, for the dyestuff data in Davies(1949, p74), the opposite appears to be the case. I am a worried that the Bayesian estimators of the varian
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