具有相依结构离散时间模型破产概率的上界

研究两类具有相依结构的离散时间风险模型的破产概率问题.其中,索赔和利率过程假设为2个不同的自回归移动平均模型.利用更新递归技巧,首先得到了该模型下破产概率所满足的递归方程.然后,根据该递归方程得到了破产概率的上界估计.最后对两类风险模型的破产概率的上界进行了比较.     

误差合成空间固定系数模型的多阶段广义最小二乘估计

本文对时变系数的空间面板数据模型进行了研究,所研究的模型利用扰动项中的空间个体成分将不同时期的方程联系起来,同时,自变量系数和空间自回归系数是时变的,但不会随着观测个体的变动而变动。本文利用基于可行的广义最小二乘估计的多阶段方法对模型参数进行了估计,研究了估计量的大样本性质,并利用Monte Carlo方法模拟了其小样本性质。模拟结果表明,估计量的渐近性质随着样本容量的增加而改善。对中国省级地区间财税策略互动行为的实证案例也体现了本文理论模型的应用价值。     

改进的pair-copula参数估计方法在经济研究中的应用

对于pair-copula中的参数估计,大多假设copula函数的参数和条件变量独立,将参数简化成一个不依赖于条件变量的常数.本文假设copula函数的参数和条件变量不独立,该参数是以条件变量为自变量的一元函数.应用该方法实证分析了“克强指数”三个指标铁路货运量、工业用电量和贷款发放量的对数增长率之间的关系,研究发现该方法优于简化的pair-copula参数估计,并且得出在固定铁路货运量不变时,工业用电量和银行贷款发放量成负相关关系,且这种负相关性随铁路货运量增加而减弱.     

Determination/estimation of an optimal replacement interval under minimal repair

In this paper the problem of estimation of an optimal replacement interval for a system which is minimally repaired at failures is studied. The problem is investigated both under a parametric and a nonparametric form of the failure intensity of the system. It is assumed that observational data from n systems are available. Some asymptotic results are shown. A graphical procedure for determining/estimating an optimal replacement interval is presented. The procedure is particularly valuable for sensitivity analyses, for example with respect to the costs involved.     

Kumaraswamy分布的经验Bayes检验

主要对Kumaraswamy分布分别在绝对值损失和加权平方损失下利用核估计构造了参数相应的经验Bayes(EB)单侧检验函数,在适当的条件下证明了所提出的EB检验函数是渐近最优的,并获得了EB检验函数的收敛速度.     

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??In this paper, we estimate parameters of gamma life distribution and normal life distribution by EM algorithm based on Type-II hybrid censored data. The covariance matrices are derived as well. Some numerical examples are also presented for illustration.     

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??In this paper, we concern with the estimation problem for the Pareto distribution based on progressive Type-II interval censoring with random removals. We discuss the maximum likelihood estimation of the model parameters. Then, we show the consistency and asymptotic normality of maximum likelihood estimators based on progressive Type-II interval censored sample.     

Functional Central Limit Theorems for Triangular Arrays of Function-Indexed Processes under Uniformly Integrable Entropy Conditions

Functional central limit theorems for triangular arrays of rowwise independent stochastic processes are established by a method replacing tail probabilities by expectations throughout. The main tool is a maximal inequality based on a preliminary version proved by P. Gaenssler and Th. Schlumprecht. Its essential refinement used here is achieved by an additional inequality due to M. Ledoux and M. Talagrand. The entropy condition emerging in our theorems was introduced by K. S. Alexander, whose functional central limit theorem for so-calledmeasure-like processeswill be also regained. Applications concern, in particular, so-calledrandom measure processeswhich include function-indexed empirical processes and partial-sum processes (with random or fixed locations). In this context, we obtain generalizations of results due to K. S. Alexander, M. A. Arcones, P. Gaenssler, and K. Ziegler. Further examples include nonparametric regression and intensity estimation for spatial Poisson processes.