Analysis of some ruin-related quantities in a Markov-modulated risk model

In this paper, we study the joint Laplace transform and probability generating function of some random quantities that occur in each environment state by the time of ruin in a Markov-modulated risk process. These quantities include the duration spent in each state, the number of claims and the aggregate amount of claims that occurred in each state by the time of ruin. Explicit formulae for the joint transforms, given the initial surplus, and the initial and terminal environment states, are expressed in terms of a matrix version of the scale function. Moments and covariances of these ruin-related quantities are obtained and numerical illustrations are presented. The joint transform of the duration spent in each state, the number of claims, and the aggregate amount of claims that occurred in each state by the time the surplus attains a certain level are also investigated.     

Multivariate process dispersion monitoring without subgrouping

The memory-type adaptive and non-adaptive control charts are among the best control charts for detecting small-to-moderate changes in the process parameter(s). In this paper, we propose the Crosier CUSUM (CCUSUM), EWMA, adaptive CCUSUM (ACCUSUM) and adaptive EWMA (AEWMA) charts for efficiently monitoring the changes in the covariance matrix of a multivariate normal process without subgrouping. Using extensive Monte Carlo simulations, the length characteristics of these control charts are computed. It turns out that the ACCUSUM and AEWMA charts perform uniformly and substantially better than the CCUSUM and EWMA charts when detecting a range of shift sizes in the covariance matrix. Moreover, the AEWMA chart outperforms the ACCUSUM chart. A real dataset is used to explain the implementation of the proposed control charts.     

Accurate computation of single and product moments of order statistics under progressive censoring

An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable.     

Model selection in high-dimensional noisy data: a simulation study

In many practical applications, high-dimensional regression analyses have to take into account measurement error in the covariates. It is thus necessary to extend regularization methods, that can handle the situation where the number of covariates p largely exceed the sample size n, to the case in which covariates are also mismeasured. A variety of methods are available in this context, but many of them rely on knowledge about the measurement error and the structure of its covariance matrix. In this paper, we set the goal to compare some of these methods, focusing on situations relevant for practical applications. In particular, we will evaluate these methods in setups in which the measurement error distribution and dependence structure are not known and have to be estimated from data. Our focus is on variable selection, and the evaluation is based on extensive simulations.     

On MLEs of the parameters of a modified Weibull distribution based on record values

The modified Weibull distribution can be used quite effectively to model complex data from mechanical engineering or survival analysis studies that posses a monotonic or a bathtub-shape hazard rate. In this paper, we study the MLEs of the parameters of a modified Weibull distribution model in the presence of upper kth record values. The existence and uniqueness of the MLEs are proven in this case. Real data analysis is performed for illustrative purposes.     

高校学报项目化管理探究——以《辽宁工程技术大学学报》为例

为了进一步提高高校学报出版活动效率,采用项目管理的方法对高校学报出版活动进行研究。分析了高校学报项目化管理的可行性。针对高校学报出版发行活动,制定了一套切实可行的操作计划,运用WBS方法对学报出版项目工作进行层层分解,项目责任矩阵图将所分解的工作落实到有关部门或个人。为确保能够在规定的时间内实现期刊出版项目的目标,编制了学报出版项目的甘特图,利用Project软件对现有人力资源和进度进行调配,使学报出版工期在原来50天的基础上缩短了16天。可见通过运用科学的项目管理工具对高校学报出版发行活动进行管理,使得高校学报管理工作更加规范化、科学化。     

对广义正定矩阵的进一步研究

研究了ＰＤ＋ｎ及ＰＳ＋ｎ两类广义正定矩阵的某些基本性质,给出两矩阵的Ｋｒｏｎｅｃｋｅｒ积与Ｈａｄａｍａｒｄ积为ＰＤ＋ｎ类广义正定矩阵的充要条件,修正了已有结论     

完全强连通方向图的表征

本文引进了完全强连通方向图的概念,利用邻接阵给出了等价表征。     

大规模定制生产的业务流程浅析

随着经济社会的转型,企业面对着越来越强大的三股力量:客户、竞争、变化。在该三股力量的不断冲击下,企业原有的生产模式———大规模生产逐渐失去竞争优势。在这种情形下,一种新的生产模式,即大规模定制生产应运而生,并且已经在众多领域成为主流的生产模式。针对大规模定制生产的特点,可以产生出一种新的矩阵结构的业务流程,该矩阵结构的业务流程具有充分的柔性,能够很好地支撑大规模定制生产,发挥其竞争优势。     

用SOR迭代法求解Poisson方程的误差检验

通过Ｐｏｉｓｓｏｎ方程第一类边值问题差分解的Ｊａｃｏｂｉ迭代法误差检验公式，推导出了ＳＯＲ迭代法误差检验公式。计算机上模拟结果表明它与理论分析的一致性。