ASA Executive Director Ron Wasserstein is tweeting like mad man. If you're not in London, catch up on what's happening at the hashtag #FSSW2013.

ASA Executive Director Ron Wasserstein is tweeting like mad man. If you're not in London, catch up on what's happening at the hashtag #FSSW2013.

This one is no big deal in the grand scheme of things, but . . . wow! Pretty blatant. Maybe someone could endow the Raymond Keene Chair of Cut-and-Paste in the statistics department at George Mason University. Anyway, say what you want about this dude, at least he’s classy. He steals not from Wikipedia but […]The post Plaig! appeared first on Statistical Modeling, Causal Inference, and Social Science.

I'm hoping to speak at a location near you in the near future but in the meantime, here are two prior occasions you may have missed. The first is the LISA conference. LISA is Leaders in Software and Art. You'd get a flavor of this fascinating group by viewing this 7-minute video (link). I gave a 5-minute lightning talk, which starts at 4:18 on the video. All the lightning talks…

From Controversies in the Foundations of Statistics by Bradley Efron: Statistics seems to be a difficult subject for mathematicians, perhaps because its elusive and wide-ranging character mitigates against the traditional theorem-proof method of presentation. It may come as some comfort then that statistics is also a difficult subject for statisticians. Related posts: Ambiguous statistical notation […]

Once you start experimenting with many different asset allocation algorithms, the computation time of running the back-tests can be substantial. One simple way to solve the computation time problem is to run the back-tests in parallel. I.e. if the asset allocation algorithm does not use the prior period holdings to make decision about current allocation, […]

I've been playing with my new iPhone 5s for the last few weeks, and first let me just say that it's an awesome phone. Don't listen to whatever Jeff says. It's probably worth it just for the camera, but I've … Continue reading →

Check out USC's fine new Center for Applied Financial Economics, led by the indefatigable Hashem Pesaran. The first event is a fascinating conference, "Recent Developments on Forecasting Techniques for Macro and Finance." Lots of information here...

Consider the following data compression problem. Suppose we have a large data set we wish to transmit. They’re too many to send directly but luckily the precise values aren’t important. Slightly different values would work as long as the da...

Tomorrow, around noon, I will be giving a talk on predictive modeling for actuaries. In the introduction, I will get back shortly on the idea that a prediction is usually a best estimate, in the sense of getting an expected value. And because it is natural to use least square ideas. In order to illustrate all those concepts, we will use a simple dataset, with the sex, the height and…

Big data is so last year. Collecting data from all sorts of odd places and analyzing it much faster than was possible even a couple of years ago has become one of the hottest areas of the technology industry. The … Continue reading →

Guido Imbens and I write: The statistical and econometrics literature on causality is more focused on “effects of causes” than on “causes of effects.” That is, in the standard approach it is natural to study the effect of a treatment, but it is not in general possible to define the causes of any particular outcome. […]The post Why ask why? Forward causal inference and reverse causal questions appeared first on…

I failed to find Kahneman’s book in the economics section of the bookshop, so I had to ask where it was. “Oh, that’s in the psychology section.” It should have also been in the statistics section. He states that his collaboration with Amos Tversky started with the question: Are humans good intuitive statisticians? The wrong […] The post A statistical review of ‘Thinking, Fast and Slow’ by Daniel Kahneman appeared…

Gina Gruenhage has just arxived a new paper describing an algorithm we call cMDS. Here’s what it’s for: if you do any kind of data analysis you often find yourself comparing datapoints using some kind of distance metric. All’s well if you have a unique reasonable distance metric you can use, but often what you […]

I recently got an email from a colleague with the subject, “Academic research, is it all bad?” He had looked at a paper presented at a VIS workshop that people were pointing to on Twitter, and had found it lacking (“it’s just a blog posting”). While there are high-quality venues for visualization research, it’s not easy to be sure which ones are good, and which ones are lower quality.