Ranking and selection with covariates

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2017 Winter Simulation Conference (WSC)
PublisherIEEE
Pages2137-2148
ISBN (Electronic)9781538634288
StatePublished - Jan 2018

Publication series

NameSimulation Winter Conference
PublisherIEEE
ISSN (Print)0891-7736
ISSN (Electronic)1558-4305

Conference

Title2017 Winter Simulation Conference, WSC 2017
PlaceUnited States
CityLas Vegas
Period3 - 6 December 2017

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.

Citation Format(s)

Ranking and selection with covariates. / Shen, Haihui; Hong, L. Jeff; Zhang, Xiaowei.

2017 Winter Simulation Conference (WSC). IEEE, 2018. p. 2137-2148 8247946 (Simulation Winter Conference).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review