Non parametric estimation of the conditional density function with right-censored and dependent data

Non-Bayesian Estimation and Prediction under Weibull Interval Censored Data

In this paper, a one-sample point predictor of the random variable X is studied. X is the occurrence of an event in any successive visits $L_i$ and $R_i$ :i=1,2…,n (interval censoring). Our proposed method is based on finding the expected value of the conditional distribution of X given $L_i$ and $R_i$ (i=1,2…,n). To make the desired prediction, our approach is on the basis of approximating the...

Relative density estimation with censored data

Well described tools for comparing two populations via their cumulative distribution functions F and F0 are the so-called relative distribution R (t) = F { F−1 0 (t) } and the corresponding relative density (or grade density) r (t). The authors propose a kerneltype estimator for the relative density r (t) in the situation where independent samples of right-censored observations are available fr...

Parametric Conditional Monte Carlo Density Estimation

In applied density estimation problems, one often has data not only on the target variable, but also on a collection of covariates. In this paper, we study a density estimator that incorporates this additional information by combining parametric estimation and conditional Monte Carlo. We prove an approximate functional asymptotic normality result that illustrates convergence rates and the asymp...

Inhomogeneous and Anisotropic Conditional Density Estimation from Dependent Data

Tree-based Multivariate Regression and Density Estimation with Right-censored Data

We propose a unified strategy for estimator construction, selection, and performance assessment in the presence of censoring. This approach is entirely driven by the choice of a loss function for the full (uncensored) data structure and can be stated in terms of the following three main steps. (1) First, define the parameter of interest as the minimizer of the expected loss, or risk, for a full...