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Activity Number: 640 - Quantile Based Modeling for a Variety of Heteroscedastic Data
Type: Invited
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #326574 Presentation
Title: A New Approach to Censored Quantile Regression Estimation
Author(s): Naveen Naidu Narisetty*
Companies: University of Illinois at Urbana Champaign
Keywords: Quantile regression; censored data; data augmentation
Abstract:

Quantile regression provides an attractive tool for analyzing censored data, because the conditional quantile functions are often of direct interest in regression analysis, and moreover, the quantiles are often identifiable while the conditional mean functions are not. Existing methods of estimation for censored quantiles are mostly limited to left- or right-censored data, with some attempts made to extend the methods to doubly-censored data. In this article, we propose a new and unified approach, based on a variation of the data augmentation algorithm, to censored quantile regression estimation. The proposed method adapts easily to different forms of censoring including doubly censored and interval censored data and performs better than the best-known estimators with singly censored data.


Authors who are presenting talks have a * after their name.

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