Robust inference with censored survival data

Spatial Modeling of Censored Survival Data

An important issue in survival data analysis is the identification of risk factors. Some of these factors are identifiable and explainable by presence of some covariates in the Cox proportional hazard model, while the others are unidentifiable or even immeasurable. Spatial correlation of censored survival data is one of these sources that are rarely considered in the literatures. In this paper,...

Robust point pattern inference from spatially censored data

Administrative data sources are increasingly used for spatial analysis and policy formation. For example, welfare-to-work programs have stimulated demand for spatial mismatch studies using ES-202 employment files. The increased resolution gained by geo-coding the address records in administrative files can be of enormous research value when the process under study resolves over small distances....

INFERENCE ON CENSORED SURVIVAL DATA UNDER COMPETING RISKS by

Inference on Conditional Quantile Residual Life for Censored Survival Data

Robust Regression Quantiles with Censored Data

In this paper we propose a method to robustly estimate linear regression quantiles with censored data. We adjust the estimator recently developed by Portnoy by replacing the Koenker-Bassett regression quantiles with the regression depth quantiles. The resulting optimization problem is solved iteratively over a set of grid points. We show on some examples that, contrary to the Koenker-Bassett ap...