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UBC Statistics Seminar on Tuesday, September 26: An Easy-to-implement Variable Selection Method for Models Following Heredity

Tuesday, September 26, 2017 - 11:00 to 12:00
William Li, Professor for Business Teaching and Research, University of Minnesota
Statistics Seminar
Room 4192, Earth Sciences Building (2207 Main Mall)

Speaker's Page:

Abstract:  In many practical regression problems, it is desirable to select important variables with heredity constraint satisfied. In other words, when an interaction term is selected, it is preferred to select all the corresponding ain effects as well. In this paper, we propose a general strategy to maintain heredity in variable selection through a novel heredity-induced data standardization. After the standardization, any variable selection method (including stepwise selection, lasso, SCAD and others) can be applied and the selected model is automatically guaranteed to satisfy the heredity constraint. Furthermore, the same procedure works for all types of regression including linear regression, generalized linear regression and regression with censored outcome. Therefore, our proposed strategy is easy (almost effortless) to implement in practice to maintain the heredity. Simulations and real examples are used to illustrate the merits of the proposed methods.