In this paper we consider logspline density estimation for data that may be lefttruncated or right-censored. For randomly left-truncated and right-censored data the product-limit estimator is known to be a consistent estimator of the survivor function, having a faster rate of convergence than many density estimators. The product-limit estimator and B-splines are used to construct the logspline ...

Non-parametric estimation of a survival function from left truncated data subject to right censoring has been extensively studied in the literature. It is commonly assumed in such studies that the lifetime variables are a sample of independent and identically distributed random variables from the target population. This assumption is often prone to failure in practical studies. For instance, wh...

One major goal in clinical applications of time-to-event data is the estimation of survival with censored data. The usual nonparametric estimator of the survival function is the time-honored Kaplan-Meier product-limit estimator. Though this estimator has been implemented in several R packages, the development of the condSURV R package has been motivated by recent contributions that allow the es...

The Kaplan–Meier estimator is a nonparametric estimator which may be used to estimate the survival distribution function from censored data. The estimator may be obtained as the limiting case of the classical actuarial (life table) estimator, and it seems to have been first proposed by Böhmer [2]. It was, however, lost sight of by later researchers and not investigated further until the importa...

In a newsvendor inventory system, demand observations often get right censored when there are lost sales and no backordering. Demands for newsvendor-type products are often forecasted from censored observations. The Kaplan-Meier product limit estimator is the well-known nonparametric method to deal with censored data, but it is undefined beyond the largest observation if it is censored. To addr...