In this paper, we use the flexible semi-parametric model $A_1(\tau,\theta)$ introduced in Gardes-Girard-Guillou (2011) for estimating extremes of censored data. Both the censored and the censoring variables are supposed to belong to this family of distributions. Solutions for modeling the tail of censored data which are between Weibull-tail and Par...
In this paper, we propose in this study a likelihood maximization method to estimate the extreme value mixture model parameters under random censoring. The idea is a combination of two maximization steps. We consider a mixture model with two components: A Weibull distribution below the threshold u (above which the data are considered extreme) and a...
© 2019 Elsevier B.V. We revisit the estimation of the extreme value index for randomly censored data from a heavy tailed distribution. We introduce a new class of estimators which encompasses earlier proposals given in Worms and Worms (2014) and Beirlant et al. (2018), which were shown to have good bias properties compared with the pseudo maximum l...
Published in Lifetime Data Analysis
We present a new estimator of the restricted mean survival time in randomized trials where there is right censoring that may depend on treatment and baseline variables. The proposed estimator leverages prognostic baseline variables to obtain equal or better asymptotic precision compared to traditional estimators. Under regularity conditions and ran...
Published in Metrika
It is proved that within a proper class of distributions, the Pareto and the shifted exponential distribution are the only distributions with the property of no loss of information due to type-I censoring and random censoring. The equality of the information before and after censoring it is achieved only when the regularity conditions do not hold.
Published in Metrika
This paper addresses the problem of estimating, from randomly censored data subject to competing risks, the extreme value index of the (sub)-distribution function associated to one particular cause, in a heavy-tail framework. Asymptotic normality of the proposed estimator is established. This estimator has the form of an Aalen-Johansen integral and...
In this paper, we investigate the estimation of the proportional hazard premium for randomly right-censored risks. The asymptotic normality of the proposed estimator is established under mild conditions.
We revisit the estimation of the extreme value index for randomly censored data from a heavy tailed distribution. We introduce a new class of estimators which encompasses earlier proposals given in Worms and Worms (2014) and Beirlant et al. (2018), which were shown to have good bias properties compared with the pseudo maximum likelihood estimator p...
This dissertation consists of three chapters studying microeconometric methods. The first two chapters focus on models with unobserved heterogeneity, and topics include testing shape restrictions imposed by economic theory and estimating counterfactual policy effects in duration analysis. In the last chapter, predictive methods in machine learning ...
Published in International Journal of System Assurance Engineering and Management
This article deals with the classical and Bayesian estimation of the parameters of log-logistic distribution using random censorship model. The maximum likelihood estimators and the asymptotic confidence intervals based on observed Fisher information matrix of the parameters are derived. Bayes estimators of the parameters under generalized entropy ...
