JOR迭代法的收敛性

基于双严格对角占优的概念,针对线性方程组在求解时常用的JOR迭代方法,给出了JOR迭代矩阵谱半径新的上界及迭代法的收敛性准则,不仅适用于严格对角占优矩阵,还适用于双严格对角占优矩阵类,对相应迭代阵谱半径的估计更精确且扩大了JOR方法收敛参数的选取范围,并用数值例子说明了所给结果的优越性。     

求解线性互补问题的AOR算法

建立了求解线性互补问题的加速松驰迭代算法，并在一定条件下，证明了新算法的收敛性。     

储层流动孔喉下限研究

针对传统方法中储层流动孔喉下限值的确定是以经验为主,缺乏足够的理论和实验支持的问题,根据核磁共振与常规压汞实验的相关性,将核磁共振分布转化为孔喉分布图谱,在此基础上结合离心实验建立了确定储层流动孔喉下限的理论方法。通过对大庆油田某区块砂岩、砂砾岩油藏储层流动孔喉下限计算可知,储层流动孔喉下限与渗透率之间存在良好的线性正相关性,但与孔隙度相关性较差。并在毛管模型基础上利用泊谡叶方程计算了两类岩性流动孔喉下限半径的渗透率累积贡献率值,结果表明该渗透率累积贡献率值达到99.90％,从而实现了储层流动孔喉下限的快速确定,为油田的储层评价工作提供了新的参考。     

流动单元划分方法的讨论与研究

针对流动单元划分方法不断发展过程中流动层带指标法和孔喉半径r35法可能存在的弊端,通过深入研究这两种方法所表达的内在含义,发现它们都是从岩石微观孔隙结构出发,以孔喉的大小对流动单元进行分类和评价,是同一物理性质的不同表达形式。同时,由于它们只反映了储层的一个物理因素,势必导致用这两种方法划分流动单元具有局限性。从流动单元的概念出发,提出只有从连续（通）性、相似性和独立性三个方面对流动单元进行划分和评价,才能准确有效地对储层进行评价。     

A Bivariate Beta-Gamma Autoregressive Process (BVBGAR(1))

In this article, we introduce a bivariate autoregressive process with Gamma marginal distributions using the form of the BGAR(2) process (Risti?, 2005 Risti? , M. M. ( 2005 ). A Beta-Gamma autoregressive process of the second-order (BGAR(2)) . Statist. Probab. Lett. 73 : 403 – 410 . [Google Scholar]) and the Beta-Gamma transformation. Some properties of the process such as the autocovariance matrix, the autocorrelation matrix, and the spectral density matrix are derived. The unknown parameters of the process are estimated using the method of moments and the method of conditional least squares. Some numerical results of the estimators are given. We investigate nonparametric and parametric estimation of the spectral density matrix of this process.     

Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies

This article examines the behavior of equity trading volume and volatility for the individual firms composing the Standard & Poor's 100 composite index. Using multivariate spectral methods, we find that fractionally integrated processes best describe the long-run temporal dependencies in both series. Consistent with a stylized mixture-of-distributions hypothesis model in which the aggregate “news”-arrival process possesses long-memory characteristics, the long-run hyperbolic decay rates appear to be common across each volume-volatility pair.     

多源多目标环境下配送中心服务半径的确定（模型研究）

面对日益高效、快速及个性化的客户配送服务需求,配送中心服务半径的科学决策和确定十分必要。本文在分析配送中心服务半径基本含义及其影响因素的基础上,针对多源多目标环境下配送中心服务半径提出了相应的数学模型,并通过数学模型对实体环境进行模拟研究,确定了一个区域内多个配送中心配送多种商品给若干客户时的服务半径。通过本研究,旨在为配送中心服务半径的科学决策提供借鉴。     

On estimating extremal dependence structures by parametric spectral measures

Estimation of extreme value copulas is often required in situations where available data are sparse. Parametric methods may then be the preferred approach. A possible way of defining parametric families that are simple and, at the same time, cover a large variety of multivariate extremal dependence structures is to build models based on spectral measures. This approach is considered here. Parametric families of spectral measures are defined as convex hulls of suitable basis elements, and parameters are estimated by projecting an initial nonparametric estimator on these finite-dimensional spaces. Asymptotic distributions are derived for the estimated parameters and the resulting estimates of the spectral measure and the extreme value copula. Finite sample properties are illustrated by a simulation study.     

Precise asymptotics on spectral statistics of random matrices

This paper focuses on the limiting properties of the spectral statistics of Wigner matrices and sample covariance matrices. Following the ideas of Gut and Spaˇtaru (2000a, b), Gut and Steinebach (2013) and Chow (1988) on precise asymptotics of i.i.d. random variables in the context of complete convergence and moment convergence, we will establish the corresponding results on the spectral statistics of random matrices.     

A Euclidean Likelihood Estimator for Bivariate Tail Dependence

The spectral measure plays a key role in the statistical modeling of multivariate extremes. Estimation of the spectral measure is a complex issue, given the need to obey a certain moment condition. We propose a Euclidean likelihood-based estimator for the spectral measure which is simple and explicitly defined, with its expression being free of Lagrange multipliers. Our estimator is shown to have the same limit distribution as the maximum empirical likelihood estimator of Einmahl and Segers (2009 Einmahl , J. H. J. , Segers , J. ( 2009 ). Maximum empirical likelihood estimation of the spectral measure of an extreme-value distribution . Ann. Statist. 37 ( 5B ): 2953 – 2989 .[Crossref], [Web of Science ®] , [Google Scholar]). Numerical experiments suggest an overall good performance and identical behavior to the maximum empirical likelihood estimator. We illustrate the method in an extreme temperature data analysis.