Survival analysis is a subfield of statistics where the goal is to analyze and model the data where the outcome is the time until the occurrence of an event of interest. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a...

Abstract : It has been recently shown that nonparametric estimates of the additive regression function could be obtained in presence of censored data by coupling the marginal integration method with initial kernel-type Inverse Probability of Censoring Weighted estimators of the multivariate regression function (7). In this paper, we get the exact rate of strong uniform consistency for such esti...

We consider a Bayesian dynamic pricing problem with an unknown distribution of customer willingness to pay (WTP). The posted price serves as either a leftor right-censoring point of the customer’s true WTP. This two-sided censoring effect makes the Bayesian dynamic pricing problem difficult to analyze. Little is known in the literature about how to solve this problem in its general form. In thi...

Restricted mean survival time (RMST) is often of great clinical interest in practice. Several existing methods involve explicitly projecting out patient-specific survival curves using parameters estimated through Cox regression. However, it would often be preferable to directly model the restricted mean for convenience and to yield more directly interpretable covariate effects. We propose gener...

Dropout is a common occurrence in longitudinal studies. Building upon the pattern-mixture modeling approach within the Bayesian paradigm, we propose a general framework of varying-coefficient models for longitudinal data with informative dropout, where measurement times can be irregular and dropout can occur at any point in continuous time (not just at observation times) together with administr...