Optimal Bayesian Sampling Plans Based on Hybrid Type-Ⅱ Censored Samples

The Bayesian sampling plans for exponential distributions are studied based on type-Ⅱ hybrid censored samples. The optimal Bayesian sampling plan is derived under a general loss function which includes the sampling cost, time-consuming cost, salvage value,and decision loss. It is employed to determine the Bayes risk and the corresponding optimal sampling plan. An explicit expression of the Bayes risk is derived. Furthermore,for the conjugate prior distribution,the closed-form formula of the Bayes decision rule can be obtained under either the linear or quadratic decision loss.

Ⅰ型左删失下Topp-Leone分布的参数及可靠度函数的估计

Firstly, the maximum likelihood estimate and asymptotic confidence interval of the unkown parameter for the Topp-Leone distribution are obtained under Type-I left censored samples, furthermore, the asymptotic confidence interval of reliability function is obtained based on monotonicity. Secondly, under different loss functions, the Bayesian estimates of the unkown parameter and reliability function are obtained, and the expected mean square errors of Bayesian estimates are calculated. Monte-Carlo method is used to calculate the mean values and relative errors of the estimates. Finally, an example of life data is analyzed by using the statistical method in this paper.

Wavelet Estimation in Heteroscedastic Model Under Censored Samples

Consider the heteroscedastic regression model Yi = g（xi） ＋ σiei, 1 ≤ i ≤ n, where σi^2 = f（ui）, here （xi, ui） being fixed design points, g and f being unknown functions defined on [0, 1], ei being independent random errors with mean zero. Assuming that Yi are censored randomly and the censored distribution function is known or unknown, we discuss the rates of strong uniformly convergence for wavelet estimators of g and f, respectively. Also, the asymptotic normality for the wavelet estimators of g is investigated.

Exponentiated Generalized Inverse Flexible Weibull Distribution:Bayesian and Non-Bayesian Estimation Under Complete and TypeⅡCensored Samples with Applications

In this paper,a new 4-parameter exponentiated generalized inverse flexible Weibull distribution is proposed.Some of its statistical properties are studied.The aim of this paper is to estimate the model parameters via several approaches,namely,maximum likelihood,maximum product spacing and Bayesian.According to Bayesian approach,several techniques are used to get the Bayesian estimators,namely,standard error function,Linex loss function and entropy loss function.The estimation herein is based on complete and censored samples.Markov Chain Monte Carlo simulation is used to discuss the behavior of the estimators for each approach.Finally,two real data sets are analyzed to obtain the flexibility of the proposed model.

Estimating an Exponential Scale Parameter Under Double Censoring

We consider estimation of the scale parameter of a two-parameter exponential distribution on the basis of doubly censored data.Classes of estimators,improving upon the minimum risk equivariant estimator,are derived under an arbitrary strictly convex loss function.Some existing dominating procedures are shown to belong to the proposed classes of estimators.