An algorithm for the exact likelihood of periodic autoregressive moving average models

An algorithm to compute the autocovariance functions of periodic autoregressive moving average models is proposed. As a result, an easily implemented algorithm for the exact likelihood of these models is rendered possible.     

Testing linear hypotheses in high-dimensional regressions

For a multivariate linear model, Wilk's likelihood ratio test (LRT) constitutes one of the cornerstone tools. However, the computation of its quantiles under the null or the alternative hypothesis requires complex analytic approximations, and more importantly, these distributional approximations are feasible only for moderate dimension of the dependent variable, say p≤20. On the other hand, assuming that the data dimension p as well as the number q of regression variables are fixed while the sample size n grows, several asymptotic approximations are proposed in the literature for Wilk's Λ including the widely used chi-square approximation. In this paper, we consider necessary modifications to Wilk's test in a high-dimensional context, specifically assuming a high data dimension p and a large sample size n. Based on recent random matrix theory, the correction we propose to Wilk's test is asymptotically Gaussian under the null hypothesis and simulations demonstrate that the corrected LRT has very satisfactory size and power, surely in the large p and large n context, but also for moderately large data dimensions such as p=30 or p=50. As a byproduct, we give a reason explaining why the standard chi-square approximation fails for high-dimensional data. We also introduce a new procedure for the classical multiple sample significance test in multivariate analysis of variance which is valid for high-dimensional data.     

Further Thoughts on the Utility of Risk Matrices

Risk matrices are commonly encountered devices for rating hazards in numerous areas of risk management. Part of their popularity is predicated on their apparent simplicity and transparency. Recent research, however, has identified serious mathematical defects and inconsistencies. This article further examines the reliability and utility of risk matrices for ranking hazards, specifically in the context of public leisure activities including travel. We find that (1) different risk assessors may assign vastly different ratings to the same hazard, (2) even following lengthy reflection and learning scatter remains high, and (3) the underlying drivers of disparate ratings relate to fundamentally different worldviews, beliefs, and a panoply of psychosocial factors that are seldom explicitly acknowledged. It appears that risk matrices when used in this context may be creating no more than an artificial and even untrustworthy picture of the relative importance of hazards, which may be of little or no benefit to those trying to manage risk effectively and rationally.     

对合矩阵与对角矩阵相似的一种证法

我们利用向量组的线性相关性以及分块矩阵的运算性质给出了下列命题的另一种有趣的证法:若n阶对合矩阵A满足条件秩(A In)=r,则A相似于对角矩阵diag{Ir,-In-r}.这种证法连同Schmidt标准正交化方法一起,还可以用来证明:当上述矩阵A是实对称(Hermite)矩阵时,A正交(酉)相似于对角矩阵diag{Ir,-n-r}.     

二维CWH-M矩阵在空频OCDMA网络中的应用

对一种新的补沃尔什哈达码-M(CWH-M)二维码,在空频二维OCDMA系统中的应用进行了研究, 实现了该码字的编解码方案和系统设计．理论分析得到该二维码在理论上可以实现完全正交,彻底消除多用户干扰．仿真结果表明了基于CWH-M二维码OCDMA系统性能比采用一维编解码方案和采用简单开关控制的二维编解码方案有了显著提高。     

对合矩阵与对角矩阵相似的一种证法

我们利用向量组的线性相关性以及分块矩阵的运算性质给出了下列命题的另一种有趣的证法：若n阶对合矩阵A满足条件秩（A＋In）=r，则A相似于对角矩阵diag{Ir，-In-r}．这种证法连同Schmidt标准正交化方法一起，还可以用来证明：当上述矩阵A是实对称（Hermite）矩阵时，A正交（酉）相似于对角矩阵diag{Ir,-n-r}．     

Influence functions of two families of robust estimators under proportional scatter matrices

In this paper, under a proportional model, two families of robust estimates for the proportionality constants, the common principal axes and their size are discussed. The first approach is obtained by plugging robust scatter matrices on the maximum likelihood equations for normal data. A projection- pursuit and a modified projection-pursuit approach, adapted to the proportional setting, are also considered. For all families of estimates, partial influence functions are obtained and asymptotic variances are derived from them. The performance of the estimates is compared through a Monte Carlo study.     

目共轭四元数矩阵积与Hadamard积的特征值的一些不等式

给出了一些四元数自共轭矩阵积与Ｈａｄａｍａｒｄ积的不等式．由此表明在很多情况下四元数自共轭矩阵积与Ｈａｄａｍａｒｄ积的性质是相似的．     

“M矩阵的Hadamard不等式”的注记

文献[1]研究了具有广泛实际背景的M矩阵的不等式。本文指出了文献[1]的主要结果,即其定理1和定理3为著名的Hadamard-Fischer不等式的特款。