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A flexible quantile regression model for medical costs with application to Medical Expenditure Panel Survey Study
Author(s) -
Zhao Xiaobing,
Wang Weiwei,
Liu Lei,
Shih YaChen T.
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7670
Subject(s) - covariate , medical expenditure panel survey , quantile regression , lasso (programming language) , heteroscedasticity , econometrics , regression analysis , feature selection , computer science , statistics , quantile , selection (genetic algorithm) , regression , mathematics , machine learning , economics , health care , world wide web , health insurance , economic growth
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.