October 2006

Predicting Elections

Jacob Eisenstein at MIT has developed an smart election predictor for the US Senate Elections using a Kalman Filter. The filter helps to decide how much extra weight to attach to more recent polls. Check it out here; he also has some details on the method here.

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Applied Statistics - Nan Laird & Christoph Lang

This week the Applied Statistics Workshop will present a talk by Nan Laird, Professor of Biostatistics in the Harvard School of Public Health, and Christoph Lang, Assistant Professor of Biostatistics in the Harvard School of Public Health.

Before joining the Department of Biostatistics, Professor Laird received her Ph.D. in Statistics from Harvard and was an Assistant Prof. of Statistics at Harvard. She has published extensively in Statistics in Medicine, Biostatistics, American Journal of Human Genetics and the American Journal of Epidemiology among others. Her research...

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Potential outcomes and equilibrium analysis

Mike Kellermann

Here's a question (alright, a bleg) for any economist-types out there: can you recommend any articles or books that integrate the potential outcomes framework for causal inference with the type of equilibrium analysis that is usually used in microeconomic modeling? I'm not exactly looking for cases where someone says "my comparative statics say the effect should be positive and, voila, it is!", but rather an applied article in which the potential outcomes arise naturally from the structure of the model. Or, even better, something more...

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Newcomb's Paradox: Reversing Causality?

Newcomb’s paradox is a classic problem in philosophy and also an entertaining puzzle to consider. Here is one version of the paradox. Suppose you are presented with two boxes, A and B. You are allowed to take just box A, just box B, or both A and B. There will always be $1000 in box A, and there will either be $0 or $1,000,000 in box B.

A ‘predictor’ determines the contents of box B before you have arrived, using the following plan. If the predictor believes you will pick both box A and B, then she places nothing...

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Unconscious Bias & Expert Witnesses

Jim Greiner

Quantitative expert witnesses are essential to modern litigation. But why do they disagree so often?

An excerpt from an article by Professor Franklin Fisher appears below. It’s a tad long, but it’s really worth reading. Does it ring a familiar bell with anyone out there?

“It is not, however, always easy to avoid becoming a ‘hired gun’ . . . The danger is sometimes a subtle one, stemming from a growing involvement in the case and friendship with the attorneys. For the serious professional, concerned about preserving his or her standards...

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Procrastination

Here’s an interesting piece that should help you keep your New Semester resolutions by understanding procrastination better. Sendhil Mullainathan recently used research by Dan Ariely and Klaus Wertenbroch as motivation for his undergraduate psychology and economics class. Though it’s not exactly statistics, it seems the insights could be useful for grad students and their advisors.

Ariely and Wertenbroch did several experiments to see how deadlines might help overcome procrastination. They...

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Statistics and baseball

Mike Kellermann

With the World Series about to get underway, featuring the rubber match between the Detroit Tigers and the St. Louis Cardinals (Round 1 went to the Cardinals in 1934, Round 2 to the Tigers in 1968, but maybe this is a best of five and we won't see the end until 2076), it is worth reflecting on the influence baseball has had on statistics and vice versa. I mentioned Frederick Mosteller's analysis after the 1946 World Series in a...

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Simpson’s Paradox

Jim Greiner

As a lawyer, I have to be interested not just in what quantitative principles are true, but also in how to present “truth” to people without quantitative training. To that end, HELP! One of the maddening things about statistics is Simpson’s paradox. The quantitative concept, undoubtedly well-known to most readers of this blog, is that the correlation between two variables can change sign and magnitude, depending on what is conditioned on. That is, Corr(A, B | C) might be positive, while Corr(A, B | C, D) might be negative, while Corr (A, B | C, D, E...

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