Machine Learning: Inference for High-Dimensional Regression

At the Becker Friedman Institute's machine learning conference, Larry Wasserman of Carnegie Mellon University discusses the differences between machine learning and econometrics and explores three popular prediction methods for high-dimensional problems.

[@ryanmartin304]

Here's the paper Larry mentioned at ~ 6:50 by Buja et al "Models as Approximations, a conspiracy of random regresors and model deviations against classical inference in regression can be found here: www-stat.wharton.upenn.edu/~lbrown/Papers/2015b%20Models%20as%20Approximations%20--%20A%20Conspiracy%20of%20Random%20Regressors%20and%20Model%20Deviations%20Against%20Classical%20Inference%20in%20Regression.pdf

[@kailinliu2883]

and there is also something called distribution free for nonparametric regression...?

[@chuchuzhu333]

Much appreciation for sharing this great talk, any chance we can download the slices? Thanks!

[@zeta-man]

I think you must have found these by now because they were easily available on the university's website but here's the link just in case:
bfi.uchicago.edu/wp-content/uploads/2_LarryTalk.pdf

[@mohammadsalah2307]

mark

[@c0t556]

Why are there ads in between the lecture? Shame on you Becker Friedman Institute University of Chicago TH-cam channel! Disable the ads!!! 😡😡😡😡😡😡