Great graphs of names

September 25, 2013
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From Nathan Yau. I love this stuff. It’s just wonderful, a great set of visualizations on a great topic. Offhand, the only suggestions I have are to scale the graphs or indicate in some way the trends in the total popularity of each name (as it is, I wonder if some of the variation is […]The post Great graphs of names appeared first on Statistical Modeling, Causal Inference, and Social…

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For Predictive Modeling, Big Data Is No Big Deal

September 25, 2013
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That is what I will be speaking about when I give a keynote talk a the Predictive Analytics World conference on Monday, September 30th in Boston.For one thing, data has always been big. Big is a relative concept and data has always been big relative to...

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The most interesting thing you’ll hear about Fisher today

September 25, 2013
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Nothing brings out the silliness in smart people like Quantum Mechanics; a subject I always associate with … R. A. Fisher. I confess to liking Fisher more than Bayesians should. Unlike the forgettable p-value conjurers I’ve known in person,...

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Sheldon Hackney: A Truly Great Penn Man

September 25, 2013
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Sheldon Hackney, Penn's president 1981-1993, recently passed away. See the fine coverage in the Almanac and Daily Pennsylvanian.In my younger days as a Penn undergrad, Hackney took a lot of abuse. People felt that he didn't have much backbone. Exh...

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Is most science false? The titans weigh in.

September 25, 2013
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Some of you may recall that a few months ago my colleague and I posted a paper to the ArXiv on estimating the rate of false discoveries in the scientific literature. The paper was picked up by the Tech Review and … Continue reading →

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Code review

September 25, 2013
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Code review

There was an interesting news item in Nature on code review. It describes a project by some folks at Mozilla to review the code (well, really just 200-line snippets) from 6 selected papers in computational biology. There are very brief quotes from Titus Brown and Roger Peng. I expect that the author of the item, […]

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Classical probability does not apply to quantum systems (causal inference edition)

September 25, 2013
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James Robins, Tyler VanderWeele, and Richard Gill write: Neyman introduced a formal mathematical theory of counterfactual causation that now has become standard language in many quantitative disciplines, but not in physics. We use results on causal interaction and interference between treatments (derived under the Neyman theory) to give a simple new proof of a well-known […]The post Classical probability does not apply to quantum systems (causal inference edition) appeared first…

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Know your data 14: they (may) know things about you

September 25, 2013
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Note: Act quickly. Looks like you can still get a free book courtesy of SAS from here. *** The New York Times features Acxiom, one of several data vendors that purportedly know a lot about you and me. Other key names in this sector include Experian and Equifax. What's new is that Acxiom will allow consumers to proactively "correct errors", or at least learn what is being bought and sold…

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Harmonic convergence

September 25, 2013
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Diederik Stapel gives a Ted talk.Sometimes, reality truly is a parody of reality.The post Harmonic convergence appeared first on Statistical Modeling, Causal Inference, and Social Science.

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Compute contours of the bivariate normal CDF

September 25, 2013
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Compute contours of the bivariate normal CDF

This is the last post in my recent series of articles on computing contours in SAS. Last month a SAS customer asked how to compute the contours of the bivariate normal cumulative distribution function (CDF). Answering that question in a single blog post would have resulted in a long article, [...]

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CO2 Emissions per Dollar of GDP

September 25, 2013
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CO2 Emissions per Dollar of GDP

For all the flak China receives about its greenhouse gas emissions, the average Chinese produces less than a third the amount of CO2 than his American counterpart. It just so happens that there are 1.3 billion Chinese, and 0.3 billion Americans, so China ends up producing more CO2. Carbon dioxide and other greenhouse gases, such […]

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"Bayes and Big Data" (Next Week at the Statistics Seminar)

September 24, 2013
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Attention conservation notice: Only of interest if you care a lot about computational statistics. For our first seminar of the year, we are very pleased to have a talk which will combine two themes close to the heart of the statistics department: ...

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Lab: Like a Jackknife to the Heart (Introduction to Statistical Computing)

September 24, 2013
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In which we meet the jackknife, by way of seeing how much error there is in our estimates from the last lab. Lab 4 (R) Introduction to Statistical Computing

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Debugging (Introduction to Statistical Computing)

September 24, 2013
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Lecture 8, Debugging: Debugging as differential diagnosis: characterize the bug, localize it in the code, try corrections. Tactics for characterizing the bug. Tactics for localizing the bug: traceback, print, warning, stopifnot. Test cases and dummy ...

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The Scope of Names (Introduction to Statistical Computing)

September 24, 2013
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Undelivered optional lecture on Scope: R looks for the values of names in the current environment; if it cannot find a value, it looks for the name in the environment which spawned this one, and so on up the tree to the common, global environment. As...

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Causation, Prediction and Search +20

September 24, 2013
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Attention conservation notice: Log-rolling promotion a conference at the intersection of the margins of several academic fields. I've written before about how one of Causation, Prediction and Search was one of the books which awakened my interest in...

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"Binomial Likelihoods and the Polya-Gamma Distribution" (Next Week at the Statistics Seminar)

September 24, 2013
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Attention conservation notice: Only of interest if you (1) care about computational statistics, and (2) will be in Pittsburgh next Monday. Having a talk on Bayesian computational statistics by a Dr. Scott worked so well last time, we're doing it ag...

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You stole my idea!

September 24, 2013
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You stole my idea!

Earlier today, Gareth has showed me a recent, interesting paper by Michael Sweeting (and colleagues). In the paper, Micheal et al describe their work on a R package to extend on the framework of the Continual Reassessment Method (the ori...

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My talk @ GSK

September 24, 2013
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This Thursday I'll give a talk at the GSK Statistics Forum. Erika (with whom I shared a train journey to the 2012 BayesPharma and a group walk in Oxfordshire a few years back) now works at GSK and invited me. I will talk about the model for c...

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How I view an academic talk: like a sports game

September 24, 2013
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I know this is a little random/non-statisticsy but I have been thinking about it a lot lately. Over the last couple of weeks I have been giving a bunch of talks and guest lectures here locally around the Baltimore/DC area. Each … Continue reading →

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“Instead of the intended message that being poor is hard, the takeaway is that rich people aren’t very good with money.”

September 24, 2013
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“Instead of the intended message that being poor is hard, the takeaway is that rich people aren’t very good with money.”

Interesting discussion here from Mark Palko. I think of Palko’s post as having a lot of statistical content here, although it’s hard for me to say exactly why it feels that way to me. Perhaps it has to do with the challenges of measurement, how something that would seem to be a simple problem of […]The post “Instead of the intended message that being poor is hard, the takeaway is…

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Seats half full or half empty

September 24, 2013
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Seats half full or half empty

Kevin Drum shows the following graphic (link) to illustrate where the House stood on authorizing force in Syria. What interests me is whether the semi-circle concept adds to the chart. It evokes the physical appearance of a chamber, presumably where...

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Working with intraday data

September 24, 2013
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When working with intraday data, analysts are often facing a large dataset problem. R is well equipped to deal with this but the standard approach has to be modified in some ways. Large dataset means different things to different people. I’m talking here about a dataset of less than 10 columns and 2 to 5 […]

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