Rank Score Test for Regional Quantiles Treatment Effect Detection

• Activity Number: 125 - New Nonparametric Statistical Methods for Multivariate and Clustered Data
• Type: Contributed
• Date/Time: Monday, July 30, 2018 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Yuan Sun* and Xuming He
• Companies: University of Michigan and University of Michigan
• Keywords: Treatment Effect; Quantile Regression; Rank Score; Bootstrap

Abstract

Quantile treatment effects are often considered in a quantile regression model. In this study, we focus on the problem of testing whether the treatment effects are significant for a set of quantile levels (e.g., lower quantiles). We propose a rank-based test, which is a generalization of the rank score test in quantile regression at an individual quantile level. This test statistic allows us to quantify the treatment effect for a prespecified quantile interval by integrating the regression rank score against certain trimmed score function. A model-based bootstrap method is constructed to estimate the null distribution. A simulation study is conducted to demonstrate the validity and usefulness of the proposed test. We also apply our method to analyze the 2016 US birth weight data and S&P 500 index data.