The following navigation utilizes arrow, enter, escape, and space bar key commands. Left and right arrows move through main tier links and expand / close menus in sub tiers. Up and Down arrows will open main tier menus and toggle through sub tier links. Enter and space open menus and escape closes them as well. Tab will move on to the next part of the site rather than go through menu items.
Authors: Kaiyuan Duan and Sujit K. Ghosh Mentor: Sujit Ghosh Affiliation: Department of Statistics, NC State University Address: 2311 Stinson Drive, 5109 SAS Hall Campus Box 8203 NC State University Raleigh, North Carolina 27695 919-515-2528
The conditional mean residual life (MRL) function is the expected remaining lifetime of a subject or a system given survival past a particular time point and the given a set of predictor values. This function is a very useful metric in reliability and actuarial studies when the right tail of the distribution is of interest and can be more relevant than the popularly used survivor or the hazard function. In earlier studies (e.g., see McLain and Ghosh, 2011) it has been shown that estimating conditional MRL without any underlying parametric assumption can be a computationally challenging problem as it involves numerical integrations to obtain smooth estimators. Given the lack of any dedicated computational tools, we develop an R package that not only allows for non-parametric estimation of conditional MRL functions based on censored data but also allows users to fit popular parametric (e.g., Accelerated Failure Time models) and semiparametric (e.g., Proportional Hazard models). Numerical illustrations are based on extensive simulation studies and also real case studies drawn from medical sciences and actuarial studies. Additionally, user-friendly version of the new R package is also demonstrated with numerous practical examples.
Presenter: Kaiyuan Duan
Institution: NC State University
Type: Poster
Subject: Mathematics
Status: Approved
