Central Limit Theorem approximations for the number of runs in Markov-dependent binary sequences

We consider Markov-dependent binary sequences and study various types of success runs (overlapping, non-overlapping, exact, etc.) by examining additive functionals based on state visits and transitions in an appropriate Markov chain. We establish a multivariate Central Limit Theorem for the number of these types of runs and obtain its covariance matrix by means of the recurrent potential matrix of the Markov chain. Explicit expressions for the covariance matrix are given in the Bernoulli and a simple Markov-dependent case by expressing the recurrent potential matrix in terms of the stationary distribution and the mean transition times in the chain. We also obtain a multivariate Central Limit Theorem for the joint number of non-overlapping runs of various sizes and give its covariance matrix in explicit form for Markov dependent trials.     

Correlation Coefficient Revisited

Elementary approaches to prove basic properties of the correlation coefficient are of pedagogical interest. Besides posing another proof, this article gives variations of the proofs already existing in the statistical literature     

Random coefficients regression models. a review 1

A review is given of random regression coefficients models. The emphasis is put on the problem of estimating the mean regression coefficients and the covariance matrix of the coefficients. Prediction of the individual random coefficients is not discussed. The main purpose of the review is to point to the practical aspects of the models and the problem of statistical inference in finite samples. Some problems for future research are indicated.     

Robust estimation of AR coefficients under simultaneously influencing outliers and missing values

A family of robust estimators for coefficients of Gaussian AR(p) time series under simultaneously influencing distortions of two types: outliers and missing values, is proposed. The estimators are based on special properties of the Cauchy probability distribution; consistency and the asymptotic normality of these estimators are proven. An approximate solution of the problem of minimization of the asymptotic variance within the proposed family of estimators is found. Performance of the proposed estimators is illustrated for simulated time series and for real data sets.     

双解析函数的Cauchy积分公式

建立了双解析函数的积分，得到双解析函数的Ｃａｕｃｈｙ积分定理、Ｍｏｒｅｒａ定理和Ｃａｕｃｈｙ积分公式．     

高斯定理的理解与应用

文章论述了高斯定理源于库仑定律，依赖于场强叠加原理，场强通量与场线通量均遵从高斯定理；高斯面上的场强是其内外所考虑的电荷产生的合场强；构建体系高斯面，局部高斯面求解场强问题。     

高等教育"民营化"问题的经济学思考

高等教育在新形势下进行了一系列改革,呈现了"民营化"趋势.其间出现了两个新的问题,即规模扩大后资源如何重新配置和后勤社会化后管理责任如何重新划分.本文运用经济学原理,主要是"科斯定理",分析了"民办机制"和民办高校在解决这两个问题时的优势,并提出了相应的对策建议.     

Cauchy-Schwarz不等式的应用及推广

Cauchy-Schwarz不等式是异于均值不等式的另一个重要不等式,不仅在数学分析、高等代数中应用比较广泛,在竞赛数学和工程实际计算中也有广泛的应用.研究Cauchy-Schwarz不等式的几种特殊形式,并分别给出了它们的证明,最后给出了Cauchy-Schwarz不等式的推广形式.     

Non-Normal Bivariate Distributions with Normal Marginals

The purpose of this note is to indicate that Fieller's Theorem can be expressed in the matrix formulation of the general linear model. The practical consequence is that one general computer program which can estimate the parameters and test the validity of a pertinent model, can also compute confidence limits for the ratios of any linear combinations of the parameters.     

Tail dependence for skew Laplace distribution and skew Cauchy distribution

ABSTRACT

Coefficient of tail dependence measures the strength of dependence in the tail of a bivariate distribution and it has been found useful in the risk management. In this paper, we derive the upper tail dependence coefficient for a random vector following the skew Laplace distribution and the skew Cauchy distribution, respectively. The result shows that skew Laplace distribution is asymptotically independent in upper tail, however, skew Cauchy distribution has asymptotic upper tail dependence.