On the linear combination of normal and Laplace random variables

Summary  The exact distribution of the linear combination αX+βY is derived when X and Y are normal and Laplace random variables distributed independently of each other. A program in MAPLE is provided to compute the associated percentage points.     

Correlations of functions of normal variables

Let (X₁, X₂,…, X_k, Y₁, Y₂,…, Y_k) be multivariate normal and define a matrix C by C_ij = cov(X_i, Y_j). If (i) (X₁,…, X_k) = (Y₁,…, Y_k) and (ii) C is symmetric positive definite, then 0 < varf(X₁,…, X_k) < ∞ corr(f(X₁,…, X_k),f(Y₁,…, Y_k)) > 0. Condition (i) is necessary for the conclusion. The sufficiency of (i) and (ii) follows from an infinite-dimensional version, which can also be applied to a pair of jointly normal Brownian motions.     

Asymptotically normal estimation in the linear-fractional regression problem with random errors in coefficients

We consider the problem of estimating the unknown parameter of the one-dimensional analog of the Michaelis-Menten equation when the independent variables are measured with random errors. We study the behavior of the explicit estimates that we have found earlier in the case of known independent variables and establish almost necessary conditions under which the presence of the random errors does not affect the asymptotic normality of these explicit estimates.     

On bilinear forms in normal variables

Bilinear forms in normal variables when the matrices of the forms are rectangular are considered. Explicit expressions for the cumulants, joint cumulants and joint cumulants of bilinear and quadratic forms are given. Necessary and sufficient conditions are established for the independence of two bilinear forms as well as a bilinear and a quadratic form. Special cases are shown to agree with known results.     

An identity on the maximum of a set of random variables

It is shown that if X₁, X₂, …, X_n are symmetric random variables and max(X₁, …, X_n)⁺ = max(0, X₁, …, X_n), then E[max(X₁,…,X_n)⁺]=[max(X₁,X₁,+X₂,+X₁,+X₃,…X₁,+X_n)⁺], and in the case of independent identically distributed symmetric random variables, E[max(X₁, X₂)⁺] = E[(X₁)⁺] + (1/2)E[(X₁ + X₂)⁺], so that for independent standard normal random variables, E[max(X₁, X₂)⁺] = (1/√2π)[1 + (1/√2)].     

On the minimum of several random variables

For a given sequence of real numbers , we denote the th smallest one by . Let be a class of random variables satisfying certain distribution conditions (the class contains Gaussian random variables). We show that there exist two absolute positive constants and such that for every sequence of real numbers and every , one has

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where are independent random variables from the class . Moreover, if , then the left-hand side estimate does not require independence of the 's. We provide similar estimates for the moments of as well.

     

On the suppression of variables in Boolean equations

The resultant of suppression of variables from a Boolean equation is a Boolean equation, derived from the parent equation, whose solutions are exactly those of the parent equation that do not involve the suppressed variables. Two examples in the literature are discussed, in which it is necessary to solve a Boolean equation while excluding solutions involving certain variables. In such cases it would be advantageous to solve the resultant of suppression of those variables rather than solving the original equation and filtering the desired solutions from the results.     

Estimation of the confidence limits for the quadratic forms in normal variables using a simple Gaussian distribution approximation

Summary  In this paper a simple Gaussian approximation of the distribution of the weighted sum of squared normal variables is proposed. The proposed approximation is computationally less complex compared to other known approximations. However, the convergence towards Gaussian distribution is guaranteed provided the weights comply with certain limit conditions. The suggested approximation is applied to the calculation of confidence limits of the quadratic forms in normal variables. These problems can be encountered in a number of statistical decision making tasks. The accuracy of the estimated confidence limit is investigated on several simulation examples.     

On a generalized mixture of standard normal and skew normal distributions

Here we propose a new class of distributions as a generalized mixture of standard normal and skew normal distributions (GMNSND) and study some of its properties by deriving its characteristic function, mean, variance, coefficient of skewness etc. Further, certain reliability aspects of GMNSND are studied and a location scale extension of GMNSND is considered. The estimation of the parameters of this extended GMNSND by the method of maximum likelihood is discussed.     

Asymptotics for self-normalized random products of sums of i.i.d. random variables

Let be a sequence of independent and identically distributed positive random variables, which is in the domain of attraction of the normal law, and tn be a positive, integer random variable. Denote , , where denotes the sample mean. Then we show that the self-normalized random product of the partial sums, , is still asymptotically lognormal under a suitable condition about tn.     

On linear functions of certain noncentral versions of independent gamma variables

A general Laplace transform and its inverse and their generalizations are considered in this article. This inverse contains the density functions of quadratic expressions in nonsingular as well as singular normal variables and a non-central version of linear functions of gamma variables, among others. Various representations of the inverse in power series, in gamma series, in Laguerre polynomials, in hypergeometric functions and in zonal polynomials are also discussed.     

舍选法的几何解释及其应用

本文给出了统计模拟中随机数生成之舍选法的几何解释 ,并将其应用到三角形分布和指数型分布的随机数生成算法中     

On estimation and control of errors of the Mann iteration process

The purpose of this note is to study the estimation of errors of the Mann iterative process with random errors. It is shown that the accumulative errors in iterative process is bounded and the errors is controllable with some conditions.     

The jackknife procedure for the probabilities of misclassification

Seven estimators for the probabilities of misclassification associated with the linear discriminant function are considered. Four of them are known in the literature. The remaining three are constructed through the Jackknife Procedure. An empirical investigation is conducted to evaluate the relative merits of these estimators. Summary of the results is presented.     

常见连续型统计分布的一点注记

正态分布是概率论与数理统计中最重要的一个分布,本文讨论了常见的连续型统计分布与标准正态分布间的关系,结果表明:几乎所有的常见连续型统计分布都是标准正态分布的函数.     

Two tests for multivariate normality based on the characteristic function

We present two tests for multivariate normality. The presented tests are based on the Lévy characterization of the normal distribution and on the BHEP tests. The tests are affine invariant and consistent. We obtain the asymptotic null distribution of the test statistics using some results about generalized one-sample U-statistics, which are of independent interest.      

On the asymptotic normality of sequences of weak dependent random variables

The aim of this paper is to investigate the asymptotic normality for strong mixing sequences of random variables in the absense of stationarity or strong mixing rates. An additional condition is imposed to the coefficients of interlaced mixing. The results are applied to linear processes of strongly mixing sequences. The class of applications include filters of certain Gaussian sequences.Supported in part by an NSF grant, cost-sharing from the University of Cincinnati, and a Taft research grant.     

On the clustering of independent uniform random variables

We consider the number K_n of clusters at a distance level d_n ∈ (0, 1) of n independent random variables uniformly distributed in [0, 1], or the number K_n of connected components in the random interval graph generated by these variables and d_n, and, depending upon how fast d_n → 0 as n → ∞, determine the asymptotic distribution of K_n, with rates of convergence, and of related random variables that describe the cluster sizes. © 2004 Wiley Periodicals, Inc. Random Struct. Alg., 2004