Inference for a progressive stress model from Weibull distribution under progressive type-II censoring

Based on progressively type-II censored samples, this paper considers progressive stress accelerated life tests when the lifetime of an item under use condition follows the Weibull distribution with a scale parameter satisfying the inverse power law. It is assumed that the progressive stress is directly proportional to time and the cumulative exposure model for the effect of changing stress holds. Point estimation of the model parameters is obtained graphically by using Weibull probability paper plot that serves as a tool for model identification and also by using the maximum likelihood method. Interval estimation is performed by finding approximate confidence intervals (CIs) for the parameters as well as the studentized-t and percentile bootstrap CIs. Monte Carlo simulation study is carried out to investigate the precision of the estimates and compare the performance of CIs obtained. Finally, two examples are presented to illustrate our results.     

Reliability inference of stress–strength model for the truncated proportional hazard rate distribution under progressively Type-II censored samples

This paper considers the reliability inference for the truncated proportional hazard rate stress–strength model based on progressively Type-II censoring scheme. When the stress and strength variables follow the truncated proportional hazard rate distributions, the maximum likelihood estimation and the pivotal quantity estimation of stress–strength reliability are derived. Based on the percentile bootstrap sampling technique, the 95% confidence interval of stress–strength reliability is obtained, as well as the related coverage percentage. Moreover, based on the Fisher Z transformation and the modified generalized pivotal quantity, the 95% modified generalized confidence interval for the stress–strength reliability is obtained. The performance of the proposed method is evaluated by the Monte Carlo simulation. The numerical results show that the pivotal quantity estimators performs better than the maximum likelihood estimators. At last, two real datasets are analyzed by the proposed methodology for illustrative purpose. The results of real example analysis show that our model can be applied to the practical problem, the truncated proportional hazard rate distribution can fit the failure data better than other distributions, and the algorithms in this paper are suitable to handle the small sample data.     

逆高斯分布变异系数和尺度参数的统计推断研究

构造了逆高斯分布中变异系数的广义枢轴量,给出了一种参数的区间估计方法,并与MOVOER(method of variance of estimates recovery)和Bootstrap 方法进行比较;给出了多总体下尺度参数两两差的同时置信区间.模拟结果表明:在中、小样本情况下,所给的广义置信区间其覆盖概率接近置信...     

Gamma分布环境因子的统计分析

证明了Gamma分布环境因子的最大似然估计是有偏估计,且其偏差为正,进而导出了Gamma分布环境因子的近似无偏估计.利用Cornish-Fisher展开导出了Gamma分布环境因子的广义置信区间,另外也给了Gamma分布环境因子的Bootstrap-t置信区间.利用模拟方法研究了所给近似无偏估计和区间估计的精度,模拟结果显示所给近似无偏估计和区间估计的精度是相当好的.     

二项分布的参数估计问题研究

本文主要讨论二项分布的参数估计问题.对GB/T 4087.1-1983,GB/T 4087.2-1983,GB/T4087.3-1985给出的经典二项分布参数点估计、区间估计和可靠度置信下限计算方法进行了分析,指出了其中存在的问题.根据二项分布的数学表达式推导出了二项分布参数的概率分布密度函数,在此基础上提出了进行二项分布参数估计的一般方法.     

双边定时截尾样本下广义逆指数分布形状参数的估计

先给出了广义逆指数分布在双边定时截尾样本下形状参数的最大似然估计,并不能得到估计的显式表达式,但证明了参数在(0,+∞)上最大似然估计是唯一存在的.其次提出用EM算法求出形状参数的估计且该估计具有良好的收敛性,还给出了形状参数的EM估计的渐近方差和近似置信区间;最后通过数值模拟,对形状参数的最大似然估计和EM估计的效果进行了比较,说明了用EM算法求形状参数的估计是可行的,并且模拟效果相对比较好.     

均匀分布位置参数的估计量的渐近分布及应用

对区间长度为定值均匀分布位置参数的点估计量进行了研究,得到位置参数点估计量的渐近分布.讨论了渐近分布的相关性质.给出了位置参数的区间估计及其假设检验方法.     

对数正态分布基于恒加试验的参数估计

讨论对数正态分布场合有非常数尺度参数恒加试验的参数估计,由最小均方误差准则导出基于完全样本恒加试验的点估计和近似区间估计.     

Transformation-based model averaged tail area inference

In parameter estimation, it is not a good choice to select a “best model” by some criterion when there is model uncertainty. Model averaging is commonly used under this circumstance. In this paper, transformation-based model averaged tail area is proposed to construct confidence interval, which is an extension of model averaged tail area method in the literature. The transformation-based model averaged tail area method can be used for general parametric models and even non-parametric models. Also, it asymptotically has a simple formula when a certain transformation function is applied. Simulation studies are carried out to examine the performance of our method and compare with existing methods. A real data set is also analyzed to illustrate the methods.     

Parameter estimation of inverse Lindley distribution for Type-I censored data

In life testing experiments, Type-I censoring scheme has been widely used due to its simplicity and poise with considerable gain in the completion time of an experiment. This article deals with the parameter estimation of inverse Lindley distribution when the data is Type-I censored. Estimates have been obtained under both the classical and Bayesian paradigm. In the classical scenario, estimates based on maximum likelihood and maximum product of spacings coupled with their 95% asymptotic confidence interval have been obtained. Under the Bayesian set up, the point estimate is obtained by considering squared error loss function using Markov Chain Monte Carlo technique and highest posterior density intervals based on these samples are reckoned. The performance of above mentioned techniques are evaluated on the basis of their simulated risks. Further, a real data set is analysed for appraisal of aforementioned estimation techniques under the specified censoring scheme.     

对数正态分布场合有非常数尺度参数恒加试验的参数估计

讨论了对数正态分布场合有非常数尺度参数恒加寿命试验的参数估计，导出了基于全样本和定数截尾样本恒加试验的点估计和近似区间估计。     

多元正态分布百分位面和容忍限的估计

针对实践中多个指标的联合分布是多元正态分布或近似多元正态分布的情况,将一元正态分布的置信限及其容忍限的估计推广到多元正态分布的场合,提出了百分位面的置信限和分布的容忍限和及容忍域的估计方法,在一定程度上克服了以往点估计的局限性,该方法具有更大的应用价值.     

Hybrid bootstrap for mapping quantitative trait loci

The hybrid bootstrap uses resampling ideas to extend the duality approach to interval estimation for a parameter of interest when there are nuisance parameters. The confidence region constructed by the hybrid bootstrap may perform much better than the parametric bootstrap region in situations where the data provide substantial information about the nuisance parameter, but limited information about the parameter of interest. We apply this method to estimate the location of quantitative trait loci (QTL) in interval mapping model. The conditional distribution of quantitative traits, given flanked genetic marker genotypes is often assumed to be the mixture model of two phenotype distributions. The mixing proportions in the model represent the recombination rate between a genetic marker and quantitative trait loci and provides information about the unknown location of the QTL. Since recombination events are unlikely, we will have less information about the location of the QTL than other parameters. This observation makes a hybrid approach to interval estimation for QTL appealing, especially since the necessary distribution theory, which is often a challenge for mixture models, can be handled by bootstrap simulation.     

Estimating the distortion parameter of the proportional-hazard premium for heavy-tailed losses

The distortion parameter reflects the amount of loading in insurance premiums. A specific value of a given premium determines a value of the distortion parameter, which depends on the underlying loss distribution. Estimating the parameter, therefore, becomes a statistical inferential problem, which has been initiated by Jones and Zitikis [Jones, B.L., Zitikis, R., 2007. Risk measures, distortion parameters, and their empirical estimation. Insurance: Mathematics and Economics, 41, 279–297] in the case of the distortion premium and tackled within the framework of the central limit theorem. Heavy-tailed losses do not fall into this framework as they rely on the extreme-value theory. In this paper, we concentrate on a special but important distortion premium, called the proportional-hazard premium, and propose an estimator for its distortion parameter in the case of heavy-tailed losses. We derive an asymptotic distribution of the estimator, construct a practically implementable confidence interval for the distortion parameter, and illustrate the performance of the interval in a simulation study.     

Statistical Analysis in Constant-Stress Accelerated Life Tests for Generalized Exponential Distribution Based on Adaptive Type-II Progressive Hybrid Censored Data

Based on adaptive type-II progressive hybrid censored data statistical analysis for constant-stress accelerated life test (CS-ALT) with products' lifetime following two-parameter generalized exponential (GE) distribution is investigated. The estimates of the unknown parameters and the reliability function are obtained through a new method combining the EM algorithm and the least square method. The observed Fisher information matrix is achieved with missing information principle, and the asymptotic unbiased estimate (AUE) of the scale parameter is also obtained. Confidence intervals (CIs) for the parameters are derived using asymptotic normality of the estimators and the percentile bootstrap (Boot-p) method. Finally, Monte Carlo simulation study is carried out to investigate the precision of the point estimates and interval estimates, respectively. It is shown that the AUE of the scale parameter is better than the corresponding two-step estimation, and the Boot-p CIs are more accurate than the corresponding asymptotic CIs.     

A Better Confidence Interval for Kappa (κ) on Measuring Agreement between Two Raters with Binary Outcomes

Abstract

Although the kappa statistic is widely used in measuring interrater agreement, it is known that the standard confidence interval estimation behaves poorly in small samples and for nonzero kappas. Efforts have been made to improve the estimation through transformation and Edgeworth expansion (Flack 1987). The results remain unsatisfactory when kappa is far from 0, however, even with the sample size as large as 100. In this article we reparameterize the kappa statistic to reveal its relationship with the marginal probability of agreement. The reparameterization not only gives a more meaningful interpretation of kappa but also clearly demonstrates that the range of kappa depends on the marginal probabilities. Various two- and three-dimensional plots are shown to illustrate the relationship among these parameters. The immediate application is to construct a new confidence interval based on the profile variance and reparameterization. Extensive simulation studies show that the new confidence interval performs very well in almost all parameter settings even when other methods fail.     

圆内二维均匀分布的参数估计

研究了圆内二维均匀分布的参数估计,利用次序统计量得到了圆的半径的估计量与置信区间.     

Adaptive Inverse Control Using a Gradient-Projection Optimization Technique

As an application of an optimization technique, a gradient-projection method is employed to derive an adaptive algorithm for updating the parameters of an inverse which is designed to cancel the effects of actuator uncertainties in a control system. The actuator uncertainty is parametrized by a set of unknown parameters which belong to a parameter region. A desirable inverse is implemented with adaptive estimates of the actuator parameters. Minimizing an estimation error, a gradient algorithm is used to update such parameter estimates. To ensure that the parameter estimates also belong to the parameter region, the adaptive update law is designed with parameter projection. With such an adaptive inverse, desired control system performance can be achieved despite the presence of the actuator uncertainties.     

获得参数最短置信区间长度的条件

寻找统计分布中参数的最短置信区间长度往往不容易,一些文献往往讨论具体分布中参数的最短置信区间长度.本文从常用枢轴变量的形式即参数的线性函数形式和反比例函数形式出发,可以获得得到参数最短置信区间长度的两个条件,并且枢轴变量的密度函数满足一定条件时,最短置信区间长度是存在且唯一的,结论具有一般性.     

Confidence intervals for ratio of two Poisson rates using the method of variance estimates recovery

Inference based on ratio of two independent Poisson rates is common in epidemiological studies. We study the performance of a variety of unconditional method of variance estimates recovery (MOVER) methods of combining separate confidence intervals for two single Poisson rates to form a confidence interval for their ratio. We consider confidence intervals derived from (1) the Fieller’s theorem, (2) the logarithmic transformation with the delta method and (3) the substitution method. We evaluate the performance of 13 such types of confidence intervals by comparing their empirical coverage probabilities, empirical confidence widths, ratios of mesial non-coverage probability and total non-coverage probabilities. Our simulation results suggest that the MOVER Rao score confidence intervals based on the Fieller’s theorem and the substitution method are preferable. We provide two applications to construct confidence intervals for the ratio of two Poisson rates in a breast cancer study and in a study that examines coronary heart diseases incidences among post menopausal women treated with or without hormones.