Regression on Quantile Residual Life

Quantile Regression for Residual Life and Empirical Likelihood

This approach has several advantages: (1) there is no need to estimate the variance/covariance at all, which may become prohibitively complicated for other procedures that requires the estimation of such. (2) When inverting the tests to obtain confidence regions/intervals, this procedure inherits all the good properties of a likelihood ratio test. (3) Free software implementation of the test is...

Analysis on Censored Quantile Residual Life Model via Spline Smoothing.

We propose a general class of quantile residual life models, where a specific quantile of the residual life time, conditional on an individual has survived up to time t, is a function of certain covariates with their coefficients varying over time. The varying coefficients are assumed to be smooth unspecified functions of t. We propose to estimate the coefficient functions using spline approxim...

Inference on Conditional Quantile Residual Life for Censored Survival Data

Inference on Quantile Residual Life for Length-biased Survival Data

Length biased data occurs when a prevalent sampling is used to recruit subjects into a study that investigates the time from an initial event to a terminal event. Such data are usually left-truncated and right-censored. While there have been accurate and efficient methods to estimate the survival function, not much work has been done regarding the estimation of the residual life time distributi...

EXTREMAL QUANTILE REGRESSION 3 quantile regression

Quantile regression is an important tool for estimation of conditional quantiles of a response Y given a vector of covariates X. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. This paper develops a theory of quantile regression in the tails. Specifically , it obtains the large sample properties of extremal (ext...