STATISTICS COLLOQUIUM
Sangwook Kang, Visiting Associate Professor
Department of Statistics
University of Connecticut
Smoothed quantile regression for censored residual lifetime data
Abstract
In this talk, we consider a semiparametric regression modeling of quantiles for residual lifetimes at a certain time point. Quantile residual lifetimes are essential summary measures in survival analysis. Recent statistical inference procedures for fitting semiparametric quantile residual lifetime models have mostly been based on estimating functions that are nonsmooth in model parameters. Thus, obtaining point estimates and their standard errors estimates could be computationally very demanding. We propose to employ a computationally-efficient induced-smoothing procedure that smoothes nonsmooth estimating functions. Variance estimation can be done via efficient resampling procedures that uses the sandwich form of asymptotic variances. We establish the consistency and asymptotic normality of the proposed estimators. Finite sample properties are investigated via an extensive simulations studies. We illustrate our proposed methods with a dental restoration study dataset.
DATE: Wednesday, February 5, 2020
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 344
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu