Adaptive estimation of the hazard rate with multiplicative censoring

E-Bayesian Estimations of Reliability and Hazard Rate based on Generalized Inverted Exponential Distribution and Type II Censoring

Introduction      This paper is concerned with using the Maximum Likelihood, Bayes and a new method, E-Bayesian, estimations for computing estimates for the unknown parameter, reliability and hazard rate functions of the Generalized Inverted Exponential distribution. The estimates are derived based on a conjugate prior for the unknown parameter. E-Bayesian estimations are obtained based on th...

Adaptive warped kernel estimators

In this work, we develop a method of adaptive nonparametric estimation, based on "warped" kernels. The aim is to estimate a real-valued function s from a sample of random couples (X,Y ). We deal with transformed data (Φ(X), Y ), with Φ a one-to-one function, to build a collection of kernel estimators. The data-driven bandwidth selection is done with a method inspired by Goldenshluger and Lepski...

Adaptive wavelet estimation of a density from mixtures under multiplicative censoring

Model selection for hazard rate estimation in presence of censoring

Abstract This note presents an estimator of the hazard rate function based on right censored data. A collection of estimators is built from a regression-type contrast, in a general collection of linear models. Then, a penalised model selection procedure provides an estimator which satisfies an oracle inequality. In particular, we can prove that it is adaptive in the minimax sense on Hölder spaces.

Adaptive estimation of hazard functions

In this paper we obtain convergence rates for sieved maximumlikelihood estimators of the log-hazard function in a censoring model. We also establish convergence results for an adaptive version of the estimator based on the method of structural risk-minimization. Applications are discussed to tensor product spline estimators as well as to neural net and radial basis function sieves. We obtain si...