Electronic Journal of Statistics

Robust learning for optimal treatment decision with NP-dimensionality

Chengchun Shi, Rui Song, and Wenbin Lu

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Abstract

In order to identify important variables that are involved in making optimal treatment decision, Lu, Zhang and Zeng (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the misspecification of the conditional mean model. Two problems arise: (i) in a world of explosively big data, effective methods are needed to handle ultra-high dimensional data set, for example, with the dimension of predictors is of the non-polynomial (NP) order of the sample size; (ii) both the propensity score and conditional mean models need to be estimated from data under NP dimensionality.

In this paper, we propose a robust procedure for estimating the optimal treatment regime under NP dimensionality. In both steps, penalized regressions are employed with the non-concave penalty function, where the conditional mean model of the response given predictors may be misspecified. The asymptotic properties, such as weak oracle properties, selection consistency and oracle distributions, of the proposed estimators are investigated. In addition, we study the limiting distribution of the estimated value function for the obtained optimal treatment regime. The empirical performance of the proposed estimation method is evaluated by simulations and an application to a depression dataset from the STAR∗D study.

Article information

Source
Electron. J. Statist. Volume 10, Number 2 (2016), 2894-2921.

Dates
Received: November 2015
First available in Project Euclid: 13 October 2016

Permanent link to this document
http://projecteuclid.org/euclid.ejs/1476368559

Digital Object Identifier
doi:10.1214/16-EJS1178

Zentralblatt MATH identifier
06643255

Keywords
Non-concave penalized likelihood optimal treatment strategy oracle property variable selection

Citation

Shi, Chengchun; Song, Rui; Lu, Wenbin. Robust learning for optimal treatment decision with NP-dimensionality. Electron. J. Statist. 10 (2016), no. 2, 2894--2921. doi:10.1214/16-EJS1178. http://projecteuclid.org/euclid.ejs/1476368559.


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