Regression Location and Scale Estimation with Application to Censoring Slides

author: Jerome H. Friedman, Department of Statistics, Stanford University
published: Nov. 7, 2016,   recorded: August 2016,   views: 1035

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The aim of regression analysis in machine learning is to estimate the location of the distribution of an outcome variable y, given the joint values of a set of predictor variables x. This location estimate is then used as a prediction for the value of y at x. The accuracy of this prediction depends on the scale of the distribution of y at x, which in turn, usually depends on x (heteroscedasticity). A robust procedure is presented for jointly estimating both the location and scale of the distribution of y given x, as functions of x, under no assumptions concerning the relationship between the two functions. The scale function can then be used to access the accuracy of individual predictions, as well as to improve accuracy especially in the presence of censoring.

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