Estimation and goodness-of-fit for the Cox model with various types of censored data
Abbreviated Journal Title
J. Stat. Plan. Infer.
Bivariate right censored data; Bivariate data under univariate right; censoring; Bootstrap; Doubly censored data; Empirical likelihood; Goodness-of-fit; Partly interval-censored data; PROPORTIONAL HAZARDS MODEL; FAILURE TIME DATA; EMPIRICAL LIKELIHOOD; SURVIVAL FUNCTION; SELF-CONSISTENT; NONPARAMETRIC-ESTIMATION; ASYMPTOTIC; PROPERTIES; REGRESSION-ANALYSIS; WEAK-CONVERGENCE; TESTS; Statistics & Probability
The currently existing estimation methods and goodness-of-fit tests for the Cox model mainly deal with right censored data, but they do not have direct extension to other complicated types of censored data, such as doubly censored data, interval censored data, partly interval-censored data, bivariate right censored data, etc. In this article, we apply the empirical likelihood approach to the Cox model with complete sample, derive the semiparametric maximum likelihood estimators (SPMLE) for the Cox regression parameter and the baseline distribution function, and establish the asymptotic consistency of the SPMLE. Via the functional plug-in method, these results are extended in a unified approach to doubly censored data, partly interval-censored data, and bivariate data under univariate or bivariate right censoring. For these types of censored data mentioned, the estimation procedures developed here naturally lead to Kolmogorov-Smirnov goodness-of-fit tests for the Cox model. Some simulation results are presented. (C) 2010 Elsevier B.V. All rights reserved.
Journal of Statistical Planning and Inference
"Estimation and goodness-of-fit for the Cox model with various types of censored data" (2011). Faculty Bibliography 2010s. 1811.