andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-299 knowledge-graph by maker-knowledge-mining
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Introduction: This hidden assumption is a biggie.
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Introduction: This hidden assumption is a biggie.
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Introduction: Michael Collins sent along the following announcement for a talk: Fast learning algorithms for discovering the hidden structure in data Daniel Hsu, Microsoft Research 11am, Wednesday April 10th, Interschool lab, 7th floor CEPSR, Columbia University A major challenge in machine learning is to reliably and automatically discover hidden structure in data with minimal human intervention. For instance, one may be interested in understanding the stratification of a population into subgroups, the thematic make-up of a collection of documents, or the dynamical process governing a complex time series. Many of the core statistical estimation problems for these applications are, in general, provably intractable for both computational and statistical reasons; and therefore progress is made by shifting the focus to realistic instances that rule out the intractable cases. In this talk, I’ll describe a general computational approach for correctly estimating a wide class of statistical mod
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Introduction: Greg Laughlin writes: My company just wrote a blog post about the ecological fallacy. There’s a discussion about it on the Hacker News message board. Someone asks, “How do you know [if a group-level finding shouldn't be used to describe individual level behavior]?” The best answer I had was “you can never tell without the individual-level data, you should always be suspicious of group-level findings applied to individuals.” Am I missing anything? Are there any situations in which you can look at group-level qualities being ascribed to individuals and not have to fear the ecological fallacy? My reply: I think that’s right. To put it another way, consider the larger model with separate coefficients for individual-level and group-level effects. If you want, you can make an assumption that they’re equal, but that’s an assumption that needs to be justified on substantive grounds. We discuss these issues a bit in this paper from 2001. (I just reread that paper. It’s pre
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Introduction: Rama Ganesan writes: I have been using many of your demos from the Teaching Stats book . . . Do you by any chance have a nice easy dataset that I can use to show students how ‘lurking variables’ work using regression? For instance, in your book you talk about the relationship between height and salaries – where gender is the hidden variable. Any suggestions?
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Introduction: Our “arm” package in R requires Doug Bates’s “lme4″ which fits multilevel models. lme4 is currently having some problems on the Mac. But installation on the Mac can be done; it just takes a bit of work. I have two sets of instructions below. From Yu-Sung: If you have MAC OS DVD, you should install developer X code packages from it. Otherwise, install them from here . After this, do the following in R: install.packages(“lme4″, type = “source”) Then you will have lme4 in R and you can install arm without a problem. And, from David Ozonoff: I installed the lme4 package via the Package Installer but this didn’t work, of course. I then installed, via this link , gfortran which seemed to put the libraries in the right place (I had earlier installed via Fink the gcc42 compiler, so I’m not sure if this is required or not). I then ran, in R, this: install.packages(c(“Matrix”,”lme4″), repos=”http://R-Forge.R-project.org”) This does not appear to work since it wi
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Introduction: This is pretty amazing. Now I want to hear volume 3. Also is there a way to download this as I play it so I can listen when I’m offline? P.S. Typo in title fixed. P.P.S. I originally gave a different link but was led to the apparently more definitive link above (which allows direct download) from a commenter . Thanks!
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Introduction: Good stuff.
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Introduction: See here .
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