The Annals of Statistics

One-Armed Bandit Problems with Covariates

Jyotirmoy Sarkar

Full-text: Open access

Abstract

As does Woodroofe, we consider a Bayesian sequential allocation between two treatments that incorporates a covariate. The goal is to maximize the total discounted expected reward from an infinite population of patients. Although our model is more general than Woodroofe's, we are able to duplicate his main result: The myopic rule is asymptotically optimal.

Article information

Source
Ann. Statist., Volume 19, Number 4 (1991), 1978-2002.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176348382

Digital Object Identifier
doi:10.1214/aos/1176348382

Mathematical Reviews number (MathSciNet)
MR1135160

Zentralblatt MATH identifier
0757.62038

JSTOR
links.jstor.org

Subjects
Primary: 62L10: Sequential analysis

Keywords
Sequential allocation one-armed bandit problem Bayesian analysis regret myopic policy asymptotically optimal

Citation

Sarkar, Jyotirmoy. One-Armed Bandit Problems with Covariates. Ann. Statist. 19 (1991), no. 4, 1978--2002. doi:10.1214/aos/1176348382. https://projecteuclid.org/euclid.aos/1176348382


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