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??Auxiliary population information is often available in finite population inference problems, and the empirical likelihood (EL) approach has been demonstrated to be flexible and useful for such problems. The present paper concerns EL when interest centers on inference for the mean of the baseline distribution under two-sample density ratio models. Although dual EL is a convenient technical tool since it has the same maximum point and maximum likelihood as DRM-based EL, it can not combine such auxiliary information into the likelihood conveniently and may have loss of efficiency. By contrast, the classical EL approach of Qin and Lawless\ucite{21} does not have this problem and incorporate seamlessly auxiliary information. Based on the EL using auxiliary information and the dual EL methods, we construct both point and interval estimations and make a careful comparison. Though the point estimation efficiency gain obtained by the former is not noticeable, we find that they may have different performances in interval estimation. In terms of coverage accuracy, the two intervals are comparable for not or moderate skewed populations, and the EL interval using auxiliary information can be much superior for severely skewed populations.     

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Solar cell is the basic component of satellite photovoltaic panels with complicated redundant system structure. Its reliability plays an important role in the system, and its performance shows a degradation trend over time. In this paper, study is conducted for the solar cell degradation modeling and reliability analysis based on practical testing results. Specifically, we illustrate an accelerated test for the attenuation ratio character test under different accumulative irradiation levels, focusing on the heteroscedasticity of the collected testing data. A heteroscedastic linear model is proposed, and the life distribution of the photovoltaic panel is obtained by using Fiducial method. A numerical example is shown for the purpose of illustration.     

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本文综述混合效应模型参数估计方面的若干新进展. 平衡混合效应方差分析模型的协方差阵具有一定结构. 对这类模型, 文献[1]提出了参数估计的一种新方法, 称为谱分解法. 新方法的突出特点是, 能同时给出固定效应和方差分量的估计, 前者是线性的, 后者是二次的,且相互独立. 而后, 文献[2--9]证明了谱分解估计的进一步的统计性质, 同时给出了协方差阵对应的估计, 它不仅是正定阵, 而且可获得它的风险函数, 这些文献还研究了谱分解估计与方差分析估计, 极大似然估计, 限制极大似然估计以及最小范数二次无偏估计的关系. 本文综述这一方向的部分研究成果, 并提出一些待进一步研究的问题.     

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??In this paper, by applying the moment inequality for asymptotically almost negatively associated (AANA, in short) random sequence and truncated method, the equivalent conditions of complete moment convergence of the maximum partial for weighted sums of AANA random variables are obtained without assumptions of identical distribution, which generalize and improve the corresponding ones of{15},{16} and {17}, respectively.     

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??Let {X_n;\,n\ge1} be a sequence of strictly stationary \rho-mixing random variables with zero mean and finite variance. Using the weak convergence theorem and probability inequalities of \rho-mixing sequence, under some proper conditions, we obtained general laws of precise asymptotics for partial sums of \rho-mixing sequence.     

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In this paper, we establish the option pricing model under sub-fractional Brownian motion, and consider the situation of the continuous dividend payments. Firstly, Wick-It\^{o} integral and partial differential method are used to get the option price of partial differential equation, and then through variable substitution into Cauchy problem, we can get the pricing formula of European call option with dividend-paying in sub-fractional Brownian motion environment. According to the pricing formula of European call option, the European put option pricing formula is obtained. Moreover, we study the parameter estimation in the model, and consider the unbiasedness and the strong convergence of the estimator.     

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The heteroscedasticity is inevitable for the panel data modeling in economics. The two-stage estimation method is a better means to study the heteroscedasticity, in which the basis is to select only one independent variable for samples grouping, it can cause the information used is incomplete. In this paper, we propose to select several variables for grouping using variable selection method, then k-mean algorithm is used to cluster, so the samples classification can be achieved and the heteroscedasticity estimation can be obtained. The results of real example analysis show that the method presented in this paper has obvious advantages in effectiveness and feasibility.     

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??A new risk model is constructed, where the total number of claims satisfies the geometric first-order integer-valued autoregressive process. Moreover, we obtain the equation of the adjustment coefficient. We discuss the relationships among the dependence on the number of claims in each period, the adjustment coefficient, and ruin probability by numerical simulations. The results show that, with the increase of the dependence on the number of claims in each period, the adjustment coefficient decrease and ruin probability increase gradually.     

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??This paper is based on ``Pao-Lu Hsu's lecture' (2019/3/22) at Peking University and the subsequent expansion of his reports. It begins with some recollections benefited of the author from Professor Hsu, and ends with thanking to a group of professors at Peking University for their support and help over the past decades. The middle part is the theme of the talk. It gives first an overview of personal cross research. Then, from a challenge of computing, the author reports on the study looking for a larger class of complex matrices which have real spectrum. This was done mainly in the last year. It involves the fields of computation, probability, statistical mechanics and quantum mechanics Next, the paper introduces the latest development of algorithms, which is another illustration of the intersection between probability theory and computational mathematics. As the end, it also outlines the understanding of the cross study.     

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本文是对近十年来科学前沿热点问题之一的 全基因组关联研究(genome-wide association study, GWAS)的一个综述, 侧重于介绍其中所用到的统计分析方法, 讨论当前GWAS中存在的一些问题及挑战, 并就其发展前景作一个展望.     

Statistical analysis of progressive stress accelerated life test for the product of two-parameter Laplace BS fatigue life distribution under inverse power law model

Based on the product of two-parameter Laplace Birnbaum-Saunders fatigue life distribution, its failure distribution mode is theoretically derived under the progressive stress accelerated life test with inverse power law model, and then three-parameter generalized Laplace Birnbaum-Saunders fatigue life distribution is introduced. The basic properties of three-parameter generalized Laplace Birnbaum-Saunders fatigue life distribution are analyzed, and the image characteristics of its density function, failure rate function and average failure rate function are investigated. Meanwhile, the point estimate method is given for three parameters, and then the point estimates of parameters are obtained for the product of two-parameter Laplace Birnbaum-Saunders fatigue life distribution under the progressive stress accelerated life test with inverse power law model. In addition, the practical example and simulation examples are illustrated to show the feasibility of the proposed method.     

一个贝叶斯离散可靠性增长模型(英)

本文提出了一个贝叶斯离散可靠性增长模型．假设一个产品的开发过程由m个阶段组成．在每一个阶段中，都进行一个成败型寿命试验．在试验结束后，再分析其结果，然后对产品进行修改或重新设计，以期提高产品的可靠性．如果产品的失效可分为不可修复的以及可修复的两种．假定产品的不可修复失效概率在各个阶段中保持相同，而可修复失效概率随着试验阶段的增加而减少．     

基于性能退化的动量轮可靠性建模与评估

为解决长寿命卫星动量轮可靠性评估无失效数据问题,研究基于性能退化的动量轮可靠性建模与评估方法.首先通过失效分析,提出以温度作为动量轮可靠性的特征量.然后利用动量轮温度的遥测数据,建立其温度随时间变化的叠合模型.为解决动量轮试验样本量不足问题,采用Bootstrap仿真方法建立动量轮寿命分布模型,评估其可靠性水平.实例表明,方法能够在无失效数据情况下,利用性能退化数据评估动量轮可靠性,且具有较高精度,为解决无失效数据条件下长寿命卫星动量轮可靠性建模与评估问题提供了一条有效途径.     

Statistical Analysis Technique of Two-parameter Generalized Exponential Sum Distribution

A new life distribution is proposed, known as ``two-parameter generalized exponential sum distribution". We study the density function and failure rate function, the average failure rate function, the image features and the numerical characteristics of the mean residual life of the distribution. Several methods of calculating point estimation of parameters are discussed. Through the Monte-Carlo simulation, we compare the precision of the point estimations. In our opinion, the best linear unbiased estimation is the most optimal solution of these methods. At the same time, several methods of calculating parameters of interval estimations are given. We also discuss the precision of interval estimations by Monte-Carlo simulation and use the best linear unbiased estimation and the best linear invariant estimation to construct interval estimations which are better than other estimation method. Finally, several simulation examples and a case of maintaining tanks is used to illustrate the application of the methods presented in this paper.     

Interval statistic based reliability analysis method on small sample hot test of satellite thruster

As one of the most important components of satellites, the thruster must maintain high reliability, but the assessment is difficult when lacking test failure data. When conducting hot test of the satellite thruster, we encountered the problem of reliability assessment on type-I censored data with only one failure, which is common for small sample tests. This paper proposes a novel interval statistic based reliability analysis method, which can fulfill the life information from the failure time to the censored time ignored by conventional methods, and improve the assessment accuracy. In this paper, a life distribution model is established by proper failure mechanism analysis and prior test information exploitation. As thrust chamber burning-through has been considered as the main failure mode, life test of the chamber coating was conducted under different thermal conditions to obtain the model parameter. Based on the interval statistic theory, a detailed derivation is illustrated. Then, reliability assessment and life prediction for the satellite thruster in both transfer orbit phase and synchronous orbit phase have been achieved, and the results show that our method performs very well, which provides an important way for dealing with test data with only one failure.     

A perturbed gamma degradation process with degradation dependent non‐Gaussian measurement errors

This paper proposes and illustrates a new perturbed gamma degradation process where the measurement error is modeled as a non‐Gaussian random variable that depends stochastically on the actual degradation level. The expression of the likelihood function for a generic set of noisy degradation measurements is derived, and the expression of the remaining useful life distribution of a degrading unit that fails when its degradation level exceeds a given threshold limit is formulated. A particle filter method is suggested, which allows one to compute the likelihood function and to estimate the remaining useful life distribution in a quick yet efficient manner. In addition, a closed‐form approximation of the perturbed gamma process is proposed to use in the special, yet meaningful, case where the standard deviation of the measurement error depends linearly on the actual degradation level. Finally, an applicative example is discussed, where the parameters of the perturbed gamma process, the remaining useful life distribution, and the mean remaining useful life of the degrading units are estimated from a set of noisy real degradation data.     

Statistical Inference for the Rayleigh distribution under progressively Type-II censoring with binomial removal

This paper takes into account the estimation for the unknown parameter of the Rayleigh distribution under Type II progressive censoring with binomial removals, where the number of units removed at each failure time follows a binomial distribution. Maximum likelihood and Bayes procedure are used to derive both point and interval estimates of the parameters involved in the model. The expected termination point to complete the censoring test is computed and analyzed under binomial censoring scheme. Numerical examples are given to illustrate the approach by means of Monte Carlo simulation. A real life data set is used for illustrative purposes in conclusion.     

Using degradation models to assess pipeline life

Longitudinal inspections of thickness at particular locations along a pipeline provide useful information to assess the remaining life of the pipeline. In applications with different mechanisms of corrosion processes, we have observed various types of general degradation paths. We present two applications of fitting a degradation model to describe the corrosion initiation and growth behavior in a pipeline. We use a Bayesian approach for parameter estimation for the degradation model. The failure‐time and remaining lifetime distributions are derived from the degradation model, and we compute Bayesian estimates and credible intervals of the failure‐time and remaining lifetime distributions for both individual segments and for the entire pipeline circuit.     

Bayesian analysis of hybrid life tests with exponential failure times

Summary A hybrid life test procedure is discussed from the Bayesian viewpoint. A total ofn items is placed on test, failed items are either not replaced or are replaced, and the test is terminated either when a pre-chosen number,K, of items have failed, or when a pre-determined time on test has been reached. Posterior and predictive distributions are obtained under the assumption of an exponential failure distribution, and point and interval estimates are given for the mean life and the life of an untested item. The results are applied to a numerical example.     

水雷保险器水压膜贮存寿命预测

本文以水雷保险器水压膜贮存失效数据为研究对象,建立了水压膜贮存统计模型,采用周期测量数据失效时刻划分的方法,将不完全失效数据转换为区间数据;提出了基于对数正态分布族和基于极值分布族的水压膜贮存寿命预测方法,对水雷其它零部件的寿命预测具有一定的参考价值。