Ranking and Selection with Covariates
Dec 10, 2017·
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0 min read
Haihui Shen
L. Jeff Hong
Xiaowei Zhang
Abstract
We consider a new ranking and selection problem in which the performance of each alternative depends on some observable random covariates. The best alternative is thus not constant but depends on the values of the covariates. Assuming a linear model that relates the mean performance of an alternative and the covariates, we design selection procedures producing policies that represent the best alternative as a function in the covariates. We prove that the selection procedures can provide certain statistical guarantee, which is defined via a nontrivial generalization of the concept of probability of correct selection that is widely used in the conventional ranking and selection setting.
Type
Publication
Proceedings of the 2017 Winter Simulation Conference, 2137–2148
Authors
Associate Professor in the Sino-US Global Logistics Institute, Shanghai Jiao Tong University.
Authors
Professor in Department of Industrial and Systems Engineering at the University of Minnesota.
Authors
I am an Associate Professor at HKUST, jointly appointed in the Department of Industrial Engineering and Decision Analytics and the Department of Economics, and the Academic Director of the MSc in FinTech program. I serve as an Associate Editor for several leading journals in the field, including Management Science, Operations Research, Navel Research Logistics, and Queueing Systems.