A class of local linear kernel density estimators based on weighted least squares kernel estimation is considered within the framework of Aalen's multiplicative intensity model. This model includes the filtered data model that, in turn, allows for truncation and/or censoring in addition to...

This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it symptotically...

A new class of local linear azard estimators based on weig ted least square kernel estimation is considered. The class includes the kernel Hazard estimator of Ramlau-Hansen (1983), which as tHe same boundary correction property as the local linear regression estimator (see Fan and Gijbels,...

We propose new procedures for estimating the univariate quantities of interest in both additive and multiplicative nonparametric marker dependent hazard models. We work with a full counting process framework that allows for left truncation and right censoring. Our procedures are based on kernels...

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Variable kernel hazard estimators are considered in the case, where the bandwidth is allowed to depend on the exposure. Simulations show, that when the exposure varies substantially, then this can improve the performance of the basic kernel smoother con-siderable. A two-stage approach for kernel...

Nielsen (1999) showed the surprising fact that a nonparametric one-dimensional hazard as a function of time can be estimated n-consistently if a high quality marker is observed. In this paper we show that the hazard relevant for predicting remaining duration time, given the current status of a...