Posts Tagged With: high dimensional data

U-Phil: (concluding the deconstruction) Wasserman / Mayo

It is traditional to end the U-Phil deconstruction discussion with the author’s remarks on the deconstruction itself.  I take this from Wasserman’s initial comment on 7/28/12, and my brief reply. I especially want to highlight the question of goals that arises.


I thank Deborah Mayo for deconstructing me and Al Franken. (And for the record, I couldn’t be further from Franken politically; I just liked his joke.)

I have never been deconstructed before. I feel a bit like Humpty Dumpty. Anyway, I think I agree with everything Deborah wrote. I’ll just clarify two points.

First, my main point was just that the cutting edge of statistics today is dealing with complex, high-dimensional data. My essay was an invitation to Philosophers to turn their analytical skills towards the problems that arise in these modern statistical problems.

Deborah wonders whether these are technical rather than foundational issues. I don’t know. When physicists went from studying medium sized, slow-moving objects to studying the very small, the very fast and the very massive, they found a plethora of interesting questions, both technical and foundational. Perhaps inference for high-dimensional, complex data can also serve as a venue for both both technical and foundational questions.

Second, I downplayed the Bayes-Frequentist perhaps more than I should have. Indeed, this debate still persists. But I also feel that only a small subset of statisticians care about the debate (because, they do what they were taught to do, without questioning it) and those that do care, will never be swayed by debate. The way I see it is that there are basically two goals:

  • Goal 1: Find ways to quantify your subjective degrees of belief.
  • Goal 2: Find procedures with good frequency properties. Continue reading
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