Empirical Likelihood Method in Survival Analysis

ISBN-13: 9781466554924 / Angielski / Twarda / 2015 / 220 str.

Mai Zhou

Opis

Add the Empirical Likelihood to Your Nonparametric Toolbox Empirical Likelihood Method in Survival Analysis explains how to use the empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out examples with the associated R code. The datasets and code are available for download on his website and CRAN. The book focuses on all the standard survival analysis topics treated with empirical likelihood, including hazard functions, cumulative distribution functions, analysis of the Cox model, and computation of empirical likelihood for censored data. It also covers semi-parametric accelerated failure time models, the optimality of confidence regions derived from empirical likelihood or plug-in empirical likelihood ratio tests, and several empirical likelihood confidence band results. While survival analysis is a classic area of statistical study, the empirical likelihood methodology has only recently been developed. Until now, just one book was available on empirical likelihood and most statistical software did not include empirical likelihood procedures. Addressing this shortfall, this book provides the functions to calculate the empirical likelihood ratio in survival analysis as well as functions related to the empirical likelihood analysis of the Cox regression model and other hazard regression models.

Spis treści

Introduction. Empirical Likelihood for Linear Functionals of Hazard. Empirical Likelihood for Linear Functionals of the Cumulative Distribution Function. Empirical Likelihood Analysis of the Cox Model. Empirical Likelihood Analysis of Accelerated Failure Time Models. Computation of Empirical Likelihood Ratio with Censored Data. Optimality of Empirical Likelihood and Plug-in Empirical Likelihood. Miscellaneous. Bibliography. Index.

O autorze

Mai Zhou is a professor in the Department of Statistics at the University of Kentucky. His research interests include large sample theory and survival analysis. He earned a PhD from Columbia University.