andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-293 knowledge-graph by maker-knowledge-mining
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Introduction: I came across this old blog entry that was just hilarious–but it’s from 2005 so I think most of you haven’t seen it. It’s the story of two people named Martin Voracek and Maryanne Fisher who in a published discussion criticized lowess (a justly popular nonlinear regression method). Curious, I looked up “Martin Voracek” on the web and found an article in the British Medical Journal whose the title promised “trend analysis.” I was wondering what statistical methods they used–something more sophisticated than lowess, perhaps? They did have one figure, and here it is: Voracek and Fisher, the critics of lowess, are fit straight lines to data to clearly nonlinear data! It’s most obvious in their leftmost graph. Voracek and Fisher get full credit for showing scatterplots, but hey . . . they should try lowess next time! What’s really funny in the graph are the little dotted lines indicating inferential uncertainty in the regression lines–all under the assumption of linearity,
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1 I came across this old blog entry that was just hilarious–but it’s from 2005 so I think most of you haven’t seen it. [sent-1, score-0.061]
2 It’s the story of two people named Martin Voracek and Maryanne Fisher who in a published discussion criticized lowess (a justly popular nonlinear regression method). [sent-2, score-1.111]
3 Curious, I looked up “Martin Voracek” on the web and found an article in the British Medical Journal whose the title promised “trend analysis. [sent-3, score-0.313]
4 ” I was wondering what statistical methods they used–something more sophisticated than lowess, perhaps? [sent-4, score-0.131]
5 They did have one figure, and here it is: Voracek and Fisher, the critics of lowess, are fit straight lines to data to clearly nonlinear data! [sent-5, score-0.654]
6 Voracek and Fisher get full credit for showing scatterplots, but hey . [sent-7, score-0.169]
7 What’s really funny in the graph are the little dotted lines indicating inferential uncertainty in the regression lines–all under the assumption of linearity, of course. [sent-11, score-0.696]
8 (You can see enlarged versions of their graphs at this link . [sent-12, score-0.075]
9 ) As usual, my own house has some glass-based construction and so it’s probably not so wise of me to throw stones, but really! [sent-13, score-0.296]
10 Not knowing about lowess is one thing, but knowing about it, then fitting a straight line to nonlinear data, then criticizing someone else for doing it right–that’s a bit much. [sent-14, score-1.243]
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same-blog 1 1.0 293 andrew gelman stats-2010-09-23-Lowess is great
Introduction: I came across this old blog entry that was just hilarious–but it’s from 2005 so I think most of you haven’t seen it. It’s the story of two people named Martin Voracek and Maryanne Fisher who in a published discussion criticized lowess (a justly popular nonlinear regression method). Curious, I looked up “Martin Voracek” on the web and found an article in the British Medical Journal whose the title promised “trend analysis.” I was wondering what statistical methods they used–something more sophisticated than lowess, perhaps? They did have one figure, and here it is: Voracek and Fisher, the critics of lowess, are fit straight lines to data to clearly nonlinear data! It’s most obvious in their leftmost graph. Voracek and Fisher get full credit for showing scatterplots, but hey . . . they should try lowess next time! What’s really funny in the graph are the little dotted lines indicating inferential uncertainty in the regression lines–all under the assumption of linearity,
2 0.22733006 1283 andrew gelman stats-2012-04-26-Let’s play “Guess the smoother”!
Introduction: Andre de Boer writes: In my profession as a risk manager I encountered this graph: I can’t figure out what kind of regression this is, would you be so kind to enlighten me? The points represent (maturity,yield) of bonds. My reply: That’s a fun problem, reverse-engineering a curve fit! My first guess is lowess, although it seems too flat and asympoty on the right side of the graph to be lowess. Maybe a Gaussian process? Looks too smooth to be a spline. I guess I’ll go with my original guess, on the theory that lowess is the most accessible smoother out there, and if someone fit something much more complicated they’d make more of a big deal about it. On the other hand, if the curve is an automatic output of some software (Excel? Stata?) then it could be just about anything. Does anyone have any ideas?
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Introduction: From 2006 : Eric Archer forwarded this document by Nick Freemantle, “The Reverend Bayes—was he really a prophet?”, in the Journal of the Royal Society of Medicine: Does [Bayes's] contribution merit the enthusiasms of his followers? Or is his legacy overhyped? . . . First, Bayesians appear to have an absolute right to disapprove of any conventional approach in statistics without offering a workable alternative—for example, a colleague recently stated at a meeting that ‘. . . it is OK to have multiple comparisons because Bayesians’ don’t believe in alpha spending’. . . . Second, Bayesians appear to build an army of straw men—everything it seems is different and better from a Bayesian perspective, although many of the concepts seem remarkably familiar. For example, a very well known Bayesian statistician recently surprised the audience with his discovery of the P value as a useful Bayesian statistic at a meeting in Birmingham. Third, Bayesians possess enormous enthusiasm fo
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Introduction: I came across this old blog entry that was just hilarious–but it’s from 2005 so I think most of you haven’t seen it. It’s the story of two people named Martin Voracek and Maryanne Fisher who in a published discussion criticized lowess (a justly popular nonlinear regression method). Curious, I looked up “Martin Voracek” on the web and found an article in the British Medical Journal whose the title promised “trend analysis.” I was wondering what statistical methods they used–something more sophisticated than lowess, perhaps? They did have one figure, and here it is: Voracek and Fisher, the critics of lowess, are fit straight lines to data to clearly nonlinear data! It’s most obvious in their leftmost graph. Voracek and Fisher get full credit for showing scatterplots, but hey . . . they should try lowess next time! What’s really funny in the graph are the little dotted lines indicating inferential uncertainty in the regression lines–all under the assumption of linearity,
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Introduction: Andre de Boer writes: In my profession as a risk manager I encountered this graph: I can’t figure out what kind of regression this is, would you be so kind to enlighten me? The points represent (maturity,yield) of bonds. My reply: That’s a fun problem, reverse-engineering a curve fit! My first guess is lowess, although it seems too flat and asympoty on the right side of the graph to be lowess. Maybe a Gaussian process? Looks too smooth to be a spline. I guess I’ll go with my original guess, on the theory that lowess is the most accessible smoother out there, and if someone fit something much more complicated they’d make more of a big deal about it. On the other hand, if the curve is an automatic output of some software (Excel? Stata?) then it could be just about anything. Does anyone have any ideas?
Introduction: Following up on our recent discussion of visually-weighted displays of uncertainty in regression curves, Lucas Leeman sent in the following two graphs: First, the basic spaghetti-style plot showing inferential uncertainty in the E(y|x) curve: Then, a version using even lighter intensities for the lines that go further from the center, to further de-emphasize the edges: P.S. More (including code!) here .
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Introduction: Solomon Hsiang writes : One of my colleagues suggested that I send you this very short note that I wrote on a new approach for displaying regression result uncertainty (attached). It’s very simple, and I’ve found it effective in one of my papers where I actually use it, but if you have a chance to glance over it and have any ideas for how to sell the approach or make it better, I’d be very interested to hear them. (Also, if you’ve seen that someone else has already made this point, I’d appreciate knowing that too.) Here’s an example: Hsiang writes: In Panel A, our eyes are drawn outward, away from the center of the display and toward the swirling confidence intervals at the edges. But in Panel B, our eyes are attracted to the middle of the regression line, where the high contrast between the line and the background is sharp and visually heavy. By using visual-weighting, we focus our readers’s attention on those portions of the regression that contain the most inform
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