@article{4056753516e64f8a9d302ef5e00d4459,
title = "A flexible quantile regression model for medical costs with application to Medical Expenditure Panel Survey Study",
abstract = "Medical costs are often skewed to the right and heteroscedastic, having a sophisticated relation with covariates. Mean function regression models with low-dimensional covariates have been extensively considered in the literature. However, it is important to develop a robust alternative to find the underlying relationship between medical costs and high-dimensional covariates. In this paper, we propose a new quantile regression model to analyze medical costs. We also consider variable selection, using an adaptive lasso penalized variable selection method to identify significant factors of the covariates. Simulation studies are conducted to illustrate the performance of the estimation method. We apply our method to the analysis of the Medical Expenditure Panel Survey dataset.",
keywords = "adaptive lasso, high-dimensional covariates, hybrid stepwise approach, partially nonlinear, quantile regression, single index, variable selection",
author = "Xiaobing Zhao and Weiwei Wang and Lei Liu and Shih, {Ya Chen T.}",
note = "Funding Information: This work was partially supported by the NSFC under Grant 11271317, Zhejiang Provincial Natural Science Foundation under Grant LY16A010007, First Class Discipline of Zhejiang-A (Zhejiang University of Finance and Economics Statistics) Grant Z0111116008/002 and AHRQ Grant R01 HS 020263. We thank Guosheng Xu for computational help. Part of the work was done when the third author was with Northwestern University. Funding Information: NSFC, Grant/Award Number: 11271317; Natural Science Foundation of Zhejiang Province, Grant/Award Number: LY16A010007; First Class Discipline of Zhejiang‐A (Zhejiang University of Finance and Economics Statistics), Grant/ Award Number: # Z0111116008/002; AHRQ, Grant/Award Number: R01 HS 020263 Funding Information: This work was partially supported by the NSFC under Grant 11271317, Zhejiang Provincial Natural Science Foundation under Grant LY16A010007, First Class Discipline of Zhejiang‐A (Zhejiang University of Finance and Economics Statistics) Grant Z0111116008/002 and AHRQ Grant R01 HS 020263. We thank Guosheng Xu for computational help. Part of the work was done when the third author was with Northwestern University. Publisher Copyright: Copyright {\textcopyright} 2018 John Wiley & Sons, Ltd.",
year = "2018",
month = jul,
day = "30",
doi = "10.1002/sim.7670",
language = "English (US)",
volume = "37",
pages = "2645--2666",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "17",
}