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Activity Number: 313
Type: Contributed
Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
Sponsor: Section for Statistical Programmers and Analysts
Abstract - #310304
Title: Composite Change-Point Estimation for Bent Line Quantile Regression
Author(s): Liwen Zhang*+ and Huixia Judy Wang and Zhongyi Zhu
Companies: Fudan University and North Carolina State University and Fudan University
Keywords: Common change point ; Bent line ; Bootstrap ; Composite quantile regression ; Rank score
Abstract:

The bent line quantile regression describes the situation where the conditional quantile function of the response is piecewise linear but still continuous in covariates. In some applications, the change points at which the quantile functions are bent tend to be the same across quantiles levels or for quantile levels lying in a certain region. To capture such commonality, we propose a composite estimation procedure to estimate model parameters and the common change point by combining information across quantiles. We establish the asymptotic properties of the proposed estimator. In addition, three different inference procedures including Wald-type test, bootstrap and rank score test are proposed and compared for hypothesis testing and the construction of confidence intervals. The finite sample performance of the proposed procedures is assessed through a simulation study and the analysis of a blood pressure and body mass index data from the 2005-2006 National Health and Nutrition Examination Survey.


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