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Title: An easy-to-implement variable selection method for models following heredity Authors:  William Li - University of Minnesota (United States) [presenting]
Abstract: In many practical regression problems, it is desirable to select important variables with heredity constraint satisfied. 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, the proposed strategy is easy to implement in practice to maintain the heredity. Simulations and real examples are used to illustrate the merits of the proposed methods. We will share some experiences on using DOE for big data problems in China.