k组多元自相关数据服从同一模型的假设检验

本文考虑了ｋ个多元同类自相关线性模型的回归系数和协方差阵相等的同时检验问题，得到了似然比检验统计量，统计量的矩及渐近中心分布。     

F_q[x]上的素数定理和Dirichlet定理及最小范数不可约多项式

讨论了F_q[x]上的zeta函数和L函数的解析性质,并在不假定黎曼猜想的情况下,导出了F_q[x]上的多项式环及其算术级数中不可约多项式的分布.然后,通过一系列的技术性处理,给出了算术级数中不可约多项式的最小范数的估计.成功地把素数定理及Dirichlet定理推广到了F_q[x]中,最重要的是,对应于最小素数问题,得到的最小范数的估计值本质上要比有理整数环上假定黎曼猜想情况下所推得的结果还好.     

从统计结果分析教职工聘岗中的心理定势与选择

高校实行岗位聘任制涉及到每位教职工的利益,在社会上人事制度改革不断推进的同时,教职工们已经比较认同了聘任制、合同制、任期制等现代人事管理模式。本文在问卷调查统计基础上,应用假设检验法、归纳、分析了教职工们对竞聘的心理活动、评价及思想顾虑;提出了改进竞聘工作的建议。     

一个除数问题的推广

本文推广了Ｒ．Ａ．Ｓｍｉｔｈ，Ｍ．Ｖ．Ｓｕｂｂａｒａｏ和Ｇ．Ｎｏｗａｋ所考虑的一个除数问题．令Ｓ＝｛（ａｉ，ｑｉ）｜ａｉ≤ｑｉ｝（ｒ≥３）．定义ｄ（ｎ；Ｓ）＝Σ（１）１，Σ（１）表示对满足ｎ＝ｍ１…ｍｒ，ｍｊ≡ａｊ（ｍｏｄｑｊ），ｊ＝１，２，…，ｒ的诸ｍｊ求和．我们求出了Σｎ≤ｘｄ（ｎ；Ｓ）的渐近公式，并得到了余项估计．     

Kolmogorov–Smirnov‐type testing for the partial homogeneity of Markov processes—with application to credit risk

In banking, the default behaviour of the counterpart is not only of interest for the pricing of transactions under credit risk but also for the assessment of a portfolio credit risk. We develop a test against the hypothesis that default intensities are chronologically constant within a group of similar counterparts, e.g. a rating class. The Kolmogorov–Smirnov‐type test builds up on the asymptotic normality of counting processes in event history analysis. The right censoring accommodates for Markov processes with more than one no‐absorbing state. A simulation study and two examples of rating systems demonstrate that partial homogeneity can be assumed, however occasionally, certain migrations must be modelled and estimated inhomogeneously. Copyright © 2007 John Wiley & Sons, Ltd.     

Testing for the maximum cell probabilities in multinomial distributions

This paper investigates one-sided hypotheses testing for p, the largest cell probability of multinomial distribution. A small sample test of Ethier (1982) is extended to the general cases. Based on an estimator of p, a kind of large sample tests is proposed. The asymptotic power of the above tests under local alternatives is derived. An example is presented at the end of this paper.     

New equipartition results for normal mode energies of anharmonic chains

The canonical and microcanonical distributions of energy among the normal modes of an anharmonic chain with nearest-neighbor interactions and free ends are examined. If the interparticle potential is an even function, then energy is distributed uniformly among the normal modes at all energy densities. If the interparticle potential is not an even function but includes quadratic, cubic, and quartic terms, then the energy sharing among the normal modes is also uniform in both the small- and large-energy density limits. At large energies, in this latter case the energy per normal mode scales as the square root of the energy density. Thus we find equipartition of energy among the normal modes of an anharmonic chain. The sum of the normal mode energies is less than the total energy of the chain.     

Multidimensional dependency measures

The problem of dependency between two random variables has been studied throughly in the literature. Many dependency measures have been proposed according to concepts such as concordance, quadrant dependency, etc. More recently, the development of the Theory of Copulas has had a great impact in the study of dependence of random variables specially in the case of continuous random variables. In the case of the multivariate setting, the study of the strong mixing conditions has lead to interesting results that extend some results like the central limit theorem to the case of dependent random variables.In this paper, we study the behavior of a multidimensional extension of two well-known dependency measures, finding their basic properties as well as several examples. The main difference between these measures and others previously proposed is that these ones are based on the definition of independence among n random elements or variables, therefore they provide a nice way to measure dependency.The main purpose of this paper is to present a sample version of one of these measures, find its properties, and based on this sample version to propose a test of independence of multivariate observations. We include several references of applications in Statistics.     

Asymptotic expansion of the null distribution of test statistic for linear hypothesis in nonnormal linear model

This paper is concerned with the null distribution of test statistic T for testing a linear hypothesis in a linear model without assuming normal errors. The test statistic includes typical ANOVA test statistics. It is known that the null distribution of T converges to χ² when the sample size n is large under an adequate condition of the design matrix. We extend this result by obtaining an asymptotic expansion under general condition. Next, asymptotic expansions of one- and two-way test statistics are obtained by using this general one. Numerical accuracies are studied for some approximations of percent points and actual test sizes of T for two-way ANOVA test case based on the limiting distribution and an asymptotic expansion.