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。     

27类蛋白质折叠子的识别及其位点的统计分析

从蛋白质的氨基酸序列出发,采用全体分类的分类策略识别27类折叠子.当使用5种矩阵打分的方法识别27类折叠子时,预测结果并不理想,进一步使用了以矩阵打分方法得到的分值及氨基酸的二肽组分共同作为参数的离散增量算法,取得了较好预测效果.总的预测成功率达到了45.43%.     

A counterexample to Beder's conjectures about Hadamard matrices

In this note we provide a counterexample which resolves conjectures about Hadamard matrices made in this journal. Beder [1998. Conjectures about Hadamard matrices. Journal of Statistical Planning and Inference 72, 7–14] conjectured that if H$\mathit{H}$ is a maximal m×n$m\times n$ row-Hadamard matrix then m is a multiple of 4; and that if n   is a power of 2 then every row-Hadamard matrix can be extended to a Hadamard matrix. Using binary integer programming we obtain a maximal 13×32$13\times 32$ row-Hadamard matrix, which disproves both conjectures. Additionally for n being a multiple of 4 up to 64, we tabulate values of m   for which we have found a maximal row-Hadamard matrix. Based on the tabulated results we conjecture that a m×n$m\times n$ row-Hadamard matrix with m?n-7$m?n-7$ can be extended to a Hadamard matrix.     

The exponentiated reduced Kies distribution: Properties and applications

Here we consider an exponentiated version of the reduced Kies distribution and discuss some of its properties. The parameters of the distribution are estimated by the method of maximum likelihood and illustrated with the help of certain real-life data sets. Asymptotic behavior of the maximum likelihood estimators of the parameters of the distribution is also studied by using certain simulated data sets.     

The Generalized Conditional Autoregressive Wishart Model for Multivariate Realized Volatility

It is well known that in finance variances and covariances of asset returns move together over time. Recently, much interest has been aroused by an approach involving the use of the realized covariance (RCOV) matrix constructed from high-frequency returns as the ex-post realization of the covariance matrix of low-frequency returns. For the analysis of dynamics of RCOV matrices, we propose the generalized conditional autoregressive Wishart (GCAW) model. Both the noncentrality matrix and scale matrix of the Wishart distribution are driven by the lagged values of RCOV matrices, and represent two different sources of dynamics, respectively. The GCAW is a generalization of the existing models, and accounts for symmetry and positive definiteness of RCOV matrices without imposing any parametric restriction. Some important properties such as conditional moments, unconditional moments, and stationarity are discussed. Empirical examples including sequences of daily RCOV matrices from the New York Stock Exchange illustrate that our model outperforms the existing models in terms of model fitting and forecasting.