Estimating the Mean of Heavy-tailed Distribution under Random Truncation

Inspired by L.Peng’s work on estimating the mean of heavy-tailed distribution in the case of completed data. we propose an alternative estimator and study its asymptotic normality when it comes to the right truncated random variable. A simulation study is executed to evaluate the finite sample behavior on the proposed estimator

Robust estimation of the extreme value index of Pareto-type distributions under random truncation with applications

In this paper, we introduce a new robust estimator for the extreme value index of Pareto-type distributions under randomly right-truncated data and establish its consistency and asymptotic normality. Our considerations are based on the Lynden-Bell integral and a useful huberized M-functional and M-estimators of the tail index. A simulation study is carried out to evaluate the robustness and the nite sample behavior of the proposed estimator. Extreme quantiles estimation is also derived and applied to real data-set of lifetimes of automobile brake pads.

Reliability Model Choice Based on AHP and Entropy Weight-TOPSIS

Aiming at the problem that some products show no failure in the random truncation test period,the mean rank order method is introduced to determine the sequence number of failure time and the method of approximate median order is used to calculate the empirical cumulative distribution function.To solve the poser that model of failure distribution of the same batch of data is not the single one,this study uses analytic hierarchy process (AHP) and entropy weight-TOPSIS,including both objective and subjective factors,to find out the datas optimun distribution.Through taking an example,It proved that this method is simple to use and has good applicability.