STICERD Econometrics Seminar Series
Statistical Theory and Algorithms for Machine Learning of Conditional Distribution Functions
Richard Spady (University of Oxford), joint with Sami Stouli, University of Bristol
Thursday 14 February 2019 14:00 - 15:30
Due to the onging coronavirus outbreak, many of our seminars and public events this year will continue as online seminars. Please check our website listings and Twitter feed @STICERD_LSE for updates.
About this event
We propose an algorithm for machine learning of conditional distribution functions for a dependent variable (Y ) with continuous support. The algorithm produces a complete description of the conditional distribution function at all observed points in the covariate (X) space, and provides a similar estimate for other possible covariate values. The descriptions it provides are quite general and are globally valid conditional densities. The algorithm is multi-layered and feed-forward. Each layer has the same statistical interpretation: Layer k takes a vector e(k-1)that is nearly perfectly marginally Gaussian and makes it more marginally Gaussian and more independent of X. It does this by applying a continuous monotonic transformation that varies depending on an observation’s X value. Each layer is estimated by an elastic net regularization of maximum likelihood. We demonstrate Wilks’ phenomenon for the composite algorithm and show how to calculate the algorithm’s effective dimension.
STICERD Econometrics seminars are held on Thursdays in term time at 14.00-15.30, ONLINE, unless specified otherwise.
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