The Steep Price of Sparsity We all love sparse estimators these days. I am referring to things like the lasso and related variable selection methods. But there is a dark side to sparsity. It’s what Hannes Leeb and Benedikt Potscher call the “return of the Hodges’ estimator.” Basically, any estimator that is capable of producing … … Continue reading →
Jeff Leek, over at Simply Statistics asks an interesting question: What are the 5 most influential statistics papers of 2000-2010? I found this to be incredibly difficult to answer. Eventually, I came up with this list: Donoho, David (2006). Compressed sensing. IEEE Transactions on Information Theory. 52, 1289-1306. Greenshtein, Eitan and Ritov, Ya’Acov. (2004). Persistence … … Continue reading →
LOST CAUSES IN STATISTICS II: Noninformative Priors I thought I would post at a higher frequency in the summer. But I have been working hard to finish some papers which has kept me quite busy. So, apologies for the paucity of posts. Today I’ll discuss another lost cause: noninformative priors. I like to say that … … Continue reading →
LOST CAUSES IN STATISTICS I: Finite Additivity I decided that I’ll write an occasional post about lost causes in statistics. (The title is motivated by Streater (2007).) Today’s post is about finitely additive probability (FAP). Recall how we usually define probability. We start with a sample space and a -algebra of events . A real-valued … … Continue reading →
SIMPSON’S PARADOX EXPLAINED Imagine a treatment with the following properties: The treatment is good for men (E1) The treatment is good for women (E2) The treatment bad overall (E3) That’s the essence of Simpson’s paradox. But there is no such treatment. Statements (E1), (E2) and (E3) cannot all be true simultaneously. Simpson’s paradox occurs when […]
In an opinion piece in the Financial Post, Stephen Ziliak goes into the land of hyperbole, declaring that all significance testing is junk science. It starts like this: I want to believe as much as the next person that particle physicists have discovered a Higgs boson, the so-called “God particle,” one with a mass of … … Continue reading →
Today is the one year anniversary of this blog. First of all, thanks to all the readers. And special thanks to commenters and guest posters. This seems like a good time to assess whether I have achieved my goals for the blog and to get suggestions on how I might proceed in year two. GOALS. … … Continue reading →
In computer science it is common to use randomized algorithms. The same is true in statistics: there are many ways that adding randomness can make things easier. But the way that randomness enters, varies quite a bit in different methods. I thought it might be interesting to collect some specific examples of statistical procedures where […]
Steve Marron is a statistician at UNC. In his younger days he was well known for his work on nonparametric theory. These days he works on a number of interesting things including analysis of structured objects (like tree-structured data) and high dimensional theory. Steve sent me a thoughtful email the other day about “Big Data” […]
Brad Efron wrote to me and posed an interesting statistical question: “Last Wednesday Diane Sawyer interviewed an Oklahoma woman who twice had had her home destroyed by a force-4 tornado. “A one in a hundred-trillion chance!” said Diane. ABC showed a nice map with the current storm’s track of destruction shaded in, about 18 miles […]
