Onk-order harmonic new better than used in expectation distributions

Summary Definitions ofk-HNBUE andK-HNWUE are introduced and the relationship with other class of life distributions is studied. Various closure properties ofk-HNBUE (k-HNWUE) are proved. Finally bounds on the moments and survival function ofk-HNBUE (k-HNWUE) are given. This research was supported by the ONR Grant N00014-78-C-0655.     

Testing whether survival function is harmonic new better than used in expectation

Summary Statistical procedures to test that a life distribution is exponential against the alternative that it is harmonic new better than used in expectation (HNBUE) are considered.     

Testing renewal new better than used life distributions based on U-test

New test-statistic for testing exponentiality against renewal new better than used (RNBU) class of life distribution based on U-statistic is studied. Selected critical values are tabulated for sample size n = 5(5)50. The Pitman asymptotic efficiency (PAE) of the test is calculated and compared with, the (PAE) of the test for new renewal better than used (NRBU) class of life distribution [see M.A.W. Mahmoud, S.M. EL-arishy, L.S. Diab, A non-parametric test of new renewal better than used class of life distributions, in: Proceedings of the International Conference on Mathematics Trends and Developments, Cairo, Egypt, vol. 4, 2002, pp. 191–203]. The power of the test is estimated by simulation at 0.05 significant level. A real example is given to elucidate the use of the proposed test. The problem in case of right-censored data is also handled.     

Multivariate tail conditional expectation for elliptical distributions

In this paper we introduce a novel type of a multivariate tail conditional expectation (MTCE) risk measure and explore its properties. We derive an explicit closed-form expression for this risk measure for the elliptical family of distributions taking into account its variance–covariance dependency structure. As a special case we consider the normal, Student-t and Laplace distributions, important and popular in actuarial science and finance. The motivation behind taking the multivariate TCE for the elliptical family comes from the fact that unlike the traditional tail conditional expectation, the MTCE measure takes into account the covariation between dependent risks, which is the case when we are dealing with real data of losses. We illustrate our results using numerical examples in the case of normal and Student-t distributions.     

一类多元回归模型参数的优于BLUE的估计

本文考虑多元线性回归模型i=1,2,B_i 未知在一定的条件下,得到参数的优于 BLUE 的估计。     

Multivariate semi-logistic distributions

Three new multivariate semi-logistic distributions (denoted by MSL⁽¹⁾, MSL⁽²⁾, and GMSL respectively) are studied in this paper. They are more general than Gumbel’s (1961) [1] and Arnold’s (1992) [2] multivariate logistic distributions. They may serve as competitors to these commonly used multivariate logistic distributions. Various characterization theorems via geometric maximization and geometric minimization procedures of the three MSL⁽¹⁾, MSL⁽²⁾ and GMSL are proved. The particular multivariate logistic distribution used in the multiple logistic regression model is introduced. Its characterization theorem is also studied. Finally, some further research work on these MSL is also presented. Some probability density plots and contours of the bivariate MSL⁽¹⁾, MSL⁽²⁾ as well as Gumbel’s and Arnold’s bivariate logistic distributions are presented in the Appendix.     

Multivariate Liouville distributions

A random vector (X₁, …, X_n), with positive components, has a Liouville distribution if its joint probability density function is of the formf(x₁ + … + x_n)x₁^a₁.1 … x_n^a_n.1 with thea_i all positive. Examples of these are the Dirichlet and inverted Dirichlet distributions. In this paper, a comprehensive treatment of the Liouville distributions is provided. The results pertain to stochastic representations, transformation properties, complete neutrality, marginal and conditional distributions, regression functions, and total positivity and reverse rule properties. Further, these topics are utilized in various characterizations of the Dirichlet and inverted Dirichlet distributions. Matrix analogs of the Liouville distributions are also treated, and many of the results obtained in the vector setting are extended appropriately.     

Multivariate semi-Weibull distributions

Some multivariate semi-Weibull (denoted by MSW) distributions including the Marshall–Olkin multivariate semi-Weibull (denoted by MO-MSW) one are introduced. They are more general than the multivariate Weibull distributions proposed by Lee [L. Lee, Multivariate distributions having Weibull properties, J. Multivariate Anal. 9 (1979) 267–277]. The Marshall–Olkin multivariate semi-Pareto (denoted by MO-MSP) distribution is also defined. Two characterization theorems for the homogeneous MSW are proved. The multivariate minima domain of partial attraction of MSW is studied, and the interrelationships between MO-MSP and MSW are examined. The MSW distribution possesses the minima-semi-stability and minima-infinite divisibility properties.     

Multivariate skew-symmetric distributions

In this paper, a class of multivariate skew distributions has been explored. Then its properties are derived. The relationship between the multivariate skew normal and the Wishart distribution is also studied.     

Multivariate cauchy distributions as locally gaussian distributions

In the present paper, we propose a definition of locally Gaussian probability distributions of random vectors based on the linearization of their conditional quantiles. We prove that the Cauchy distribution inR ⁿ is locally Gaussian and give explicit formulas for the vectors of expectations and covariance matrices of locally Gaussian approximations. We show that locally Gaussian approximations with different dimensionalities are in some sense compatible: all of them have equal corresponding correlation coefficients. For the Cauchy distribution in a Hilbert space we prove a limit theorem on the convergence of squared finite-dimensional conditional quantiles to the stable Lévy distribution. Proceedings of the XVI Seminar on Stability Problems for Stochastic Models. Part II. Eger, Hungary, 1994.     

Multivariate θ-generalized normal distributions

A new family of continuous multivariate distributions is introduced, generalizing the canonical form of the multivariate normal distribution. The well-known univariate version of this family, as developed by Box, Tiao and Lund, among others, has proven a valuable tool in Bayesian analysis and robustness studies, as well as serving as a unified model for least θ's and maximum likelihood estimates. The purpose of the family introduced here is to extend, to a degree of generality which will permit practical applications, the useful role played by the univariate family to a multidimensional setting.     

Multivariate distributions having Weibull properties

Random variables X₁ ,…, X_n are said to have a joint distribution with Weibull minimums after arbitrary scaling if min_i(a_iX_i) has a one dimensional Weibull distribution for arbitrary constants a_i > 0, i = 1,…, n. Some properties of this class are demonstrated, and some examples are given which show the existence of a number of distributions belonging to the class. One of the properties is found to be useful for computing component reliability importance. The class is seen to contain an absolutely continuous Weibull distribution which can be generated from independent uniform and gamma distributions.     

Multivariate skew-normal distributions with applications in insurance

In this paper, we discuss the skew-normal distribution as an alternative to the classical normal one in the context of both risk measurement and capital allocation. As main risk measure, we consider the tail conditional expectation (TCE). Hence, we investigate an allocation formula based on the TCE, but we also consider Wang’s [Wang, S., 2002. A set of new methods and tools for enterprise risk capital management and portfolio optimization. Working paper. SCOR reinsurance company (www.casact.com/pubs/forum/02sforum/02sf043.pdf)] allocation formula.     

Multivariate geometric stable distributions in financial applications

We describe a class of multivariate geometric stable laws that can be used in modeling multivariate financial portfolios of securities. These heavy tailed distributions are stable with respect to geometric summation and accommodate the possibility of market crashes. We look at bivariate currency exchange rates data and show that its main features, peakedness and heavy tails, are very well captured by the geometric stable model.     

Multivariate stable distributions and generating densities

The probability density function of multivariate stable distributions only applies to special accessible cases. Consequently, because of the absence of an explicit solution for their probability distribution function, applications have been limited. In this paper, we present an analytic method for generating densities to resolve this problem. Some examples and special cases are discussed.     

Multivariate subexponential distributions and their applications

We propose a new definition of a multivariate subexponential distribution. We compare this definition with the two existing notions of multivariate subexponentiality, and compute the asymptotic behaviour of the ruin probability in the context of an insurance portfolio, when multivariate subexponentiality holds. Previously such results were available only in the case of multivariate regularly varying claims.     

Multivariate Meixner classes of invariant distributions

We define multivariate Meixner classes of invariant distributions of random matrices as those whose generating functions for the associated orthogonal polynomials are of certain special integral or summation forms, generalizing the univariate Meixner classes of distributions which were first characterized by Meixner [21]. Characterization theorems and properties of these multivariate Meixner classes are established. The zonal polynomials, the extended invariant polynomials with matrix arguments, and their related results in multivariate distribution theory are utilized in the discussion.