Modelling losses and locating the tail with the Pareto Positive Stable distribution

This paper focuses on modelling the severity distribution. We directly model the small, moderate and large losses with the Pareto Positive Stable (PPS) distribution and thus it is not necessary to fix a threshold for the tail behaviour. Estimation with the method of moments is straightforward. Properties, graphical tests and expressions for value-at risk and tail value-at-risk are presented. Furthermore, we show that the PPS distribution can be used to construct a statistical test for the Pareto distribution and to determine the threshold for the Pareto shape if required. An application to loss data is presented. We conclude that the PPS distribution can perform better than commonly used distributions when modelling a single loss distribution for moderate and large losses. This approach avoids the pitfalls of cut-off selection and it is very simple to implement for quantitative risk analysis.     

Modelling losses and locating the tail with the Pareto Positive Stable distribution

This paper focuses on modelling the severity distribution. We directly model the small, moderate and large losses with the Pareto Positive Stable (PPS) distribution and thus it is not necessary to fix a threshold for the tail behaviour. Estimation with the method of moments is straightforward. Properties, graphical tests and expressions for value-at risk and tail value-at-risk are presented. Furthermore, we show that the PPS distribution can be used to construct a statistical test for the Pareto distribution and to determine the threshold for the Pareto shape if required. An application to loss data is presented. We conclude that the PPS distribution can perform better than commonly used distributions when modelling a single loss distribution for moderate and large losses. This approach avoids the pitfalls of cut-off selection and it is very simple to implement for quantitative risk analysis.     

On bounds for the mode and median of the generalized hyperbolic and related distributions

Except for certain parameter values, a closed form formula for the mode of the generalized hyperbolic (GH) distribution is not available. In this paper, we exploit results from the literature on modified Bessel functions and their ratios to obtain simple but tight two-sided inequalities for the mode of the GH distribution for general parameter values. As a special case, we deduce tight two-sided inequalities for the mode of the variance-gamma (VG) distribution, and through a similar approach we also obtain tight two-sided inequalities for the mode of the McKay Type I distribution. The analogous problem for the median is more challenging, but we conjecture some monotonicity results for the median of the VG and McKay Type I distributions, from we which we conjecture some tight two-sided inequalities for their medians. Numerical experiments support these conjectures and also lead us to a conjectured tight lower bound for the median of the GH distribution.     

Formula for the Laplace transform of the projection of a distribution on the positive semiaxis and some of its applications

We obtain a formula for the Laplace transform of the restriction of an arbitrary probability distribution on the positive semiaxis in the form of a Cauchy-type integral in infinite limits of the characteristic function of this distribution. Using this result and the estimates of the concentration function of the sum of independent random variables, we derive a representation for the Laplace transform of the restriction of the harmonic measure on the positive semiaxis. In conclusion, we present an estimate of the lower ladder height distribution for the case in which the distribution of the value of the jump in a random walk is normal.     

Asymptotics of the distribution of the integral of the positive part of the Brownian bridge for large arguments

The double Laplace transform of the distribution function of the integral of the positive part of the Brownian bridge was determined by M. Perman and J.A. Wellner, as well as the moments of this distribution. The purpose of the present paper is to determine the asymptotics of this distribution for large values of the argument, and the corresponding asymptotics of the moments.     

Simplification of joint confidence regions for the parameters of the Pareto distribution

The Pareto distribution is an important distribution in statistics, which has been widely used in economics to model the distribution of incomes. Separate interval estimations for parameters of the Pareto distribution have been well established in the literature. For a type-II right censored sample, Chen (Metrika 44:191–197, 1996) proposed a joint confidence region for the parameters. Wu (Comput Stat Data Anal 52:3779–3788, 2008) derived a joint confidence region for any type-II censored sample, but its computation is difficult. Both Wu’s and Chen’s results are simplified in this paper, and some errors are corrected. The methodology used in this article can be applied to other distributions, as long as the underlying distribution can be transformed to the standard exponential distribution in a simple way.     

Goodness-of-Fit Tests for the Inverse Gaussian Distribution Based on the Empirical Laplace Transform

This paper considers two flexible classes of omnibus goodness-of-fit tests for the inverse Gaussian distribution. The test statistics are weighted integrals over the squared modulus of some measure of deviation of the empirical distribution of given data from the family of inverse Gaussian laws, expressed by means of the empirical Laplace transform. Both classes of statistics are connected to the first nonzero component of Neyman's smooth test for the inverse Gaussian distribution. The tests, when implemented via the parametric bootstrap, maintain a nominal level of significance very closely. A large-scale simulation study shows that the new tests compare favorably with classical goodness-of-fit tests for the inverse Gaussian distribution, based on the empirical distribution function.     

Approximation of the Ultimate Ruin Probability in the Classical Risk Model Using Erlang Mixtures

In this paper, we approximate the ultimate ruin probability in the Cramér-Lundberg risk model when claim sizes have an arbitrary continuous distribution. We propose two approximation methods, based on Erlang Mixtures, which can be used for claim sizes distribution both light and heavy tailed. Additionally, using a continuous version of the empirical distribution, we develop a third approximation which can be used when the claim sizes distribution is unknown and paves the way for a statistical application. Numerical examples for the gamma, Weibull and truncated Pareto distributions are provided.     

On the rate of convergence of the distributions of certain statistics to the Laplace and student distributions

This work deals with asymptotically normal statistics. If the size of sample is replaced by a random variable that is the maximum of n independent random variables with Pareto discrete distribution, then the distribution of these statistics is asymptotically Laplacian. If the size of the sample is replaced by a random variable with negative binomial distribution, then the distribution of these statistics is asymptotically a Student distribution. In the present study, the rates of convergences of these statistics’ distributions to the corresponding limiting distributions are estimated and the constants from expressions for estimating the rates are determined more accurately.     

On a logarithmic integral and the moments of order statistics from the Weibull-geometric and half-logistic families of distributions

We provide an explicit analytical solution for a logarithmic integral in terms of the Lerch transcendent function together with the generalized Stirling numbers of the first kind. For some special cases of interest in statistical applications, the explicit solution can be expressed in terms of the polylogarithm function together with the aforementioned Stirling numbers. As a consequence, we obtain explicit expressions for the moments of order statistics from the half-logistic distribution, the Weibull-geometric distribution and the long-term Weibull-geometric distribution, which include as particular cases the extended exponential-geometric distribution and the long-term extended exponential-geometric distribution, among others. These analytical expressions are useful for computational purposes.     

On the distribution of the ratio of the largest eigenvalue to the trace of a Wishart matrix

The ratio of the largest eigenvalue divided by the trace of a p×p random Wishart matrix with n degrees of freedom and an identity covariance matrix plays an important role in various hypothesis testing problems, both in statistics and in signal processing. In this paper we derive an approximate explicit expression for the distribution of this ratio, by considering the joint limit as both p,n→∞ with p/n→c. Our analysis reveals that even though asymptotically in this limit the ratio follows a Tracy-Widom (TW) distribution, one of the leading error terms depends on the second derivative of the TW distribution, and is non-negligible for practical values of p, in particular for determining tail probabilities. We thus propose to explicitly include this term in the approximate distribution for the ratio. We illustrate empirically using simulations that adding this term to the TW distribution yields a quite accurate expression to the empirical distribution of the ratio, even for small values of p,n.     

Some Aspects of the Distributional Properties of the Expected Value of Perfect Information (EVPI)

In this paper we examine the Expected Value of Perfect Information (EVPI), with respect to the underlying probability distribution. By the method of optimal control theory we show that for any given second and third moments the underlying distribution that maximizes the EVPI is a 2-spike distribution. We find that shifting the distribution mean from zero will decrease the associated EVPI. The EVPI is further analyzed when the underlying distribution is symmetric, centred at zero.     

Tests of fit for the Rayleigh distribution based on the empirical Laplace transform

In this paper a class of goodness-of-fit tests for the Rayleigh distribution is proposed. The tests are based on a weighted integral involving the empirical Laplace transform. The consistency of the tests as well as their asymptotic distribution under the null hypothesis are investigated. As the decay of the weight function tends to infinity the test statistics approach limit values. In a particular case the resulting limit statistic is related to the first nonzero component of Neyman’s smooth test for this distribution. The new tests are compared with other omnibus tests for the Rayleigh distribution.     

A note on the moments of the Riesz-Nágy-Takács distribution

The Riesz-Nágy-Takács (RNT) distribution generalizes the dyadic Riesz-Nágy distribution that has been the subject of recent investigations. In order to study orthogonal polynomials with respect to the RNT distribution it is crucial to know the moments of the distribution. We develop a recurrence relation for these moments.     

Optimization of the contact pressure in the problem of the interaction of a punch and an elastic medium

The problem of optimizing the pressure distribution under a rigid punch, which interacts without friction with an elastic medium filling a half-space, is investigated. The shape of the punch is taken as the initial variable of the design, while the root mean square deviation of the pressure distribution, which occurs under the punch, from a certain specified distribution, plays the role of the minimized functional. The values of the total forces and moments, applied to the punch, are assumed to be given, which leads to limitations imposed on the pressure distribution by the equilibrium conditions. It is shown that the optimization problem allows of decomposition into two successively solvable problems. The first problem consists of finding the pressure distribution which makes the optimized quality functional a minimum. The second problem is reduced to the problem of obtaining directly the optimum shape of the punch that yields the pressure distribution found. The optimization problem is investigated analytically for punches of different shape in plan. The optimum shapes are given in explicit form for punches with rectangular bases.     

Bias of the structural quasi-score estimator of a measurement error model under misspecification of the regressor distribution

In a structural measurement error model the structural quasi-score (SQS) estimator is based on the distribution of the latent regressor variable. If this distribution is misspecified, the SQS estimator is (asymptotically) biased. Two types of misspecification are considered. Both assume that the statistician erroneously adopts a normal distribution as his model for the regressor distribution. In the first type of misspecification, the true model consists of a mixture of normal distributions which cluster around a single normal distribution, in the second type, the true distribution is a normal distribution admixed with a second normal distribution of low weight. In both cases of misspecification, the bias, of course, tends to zero when the size of misspecification tends to zero. However, in the first case the bias goes to zero in a flat way so that small deviations from the true model lead to a negligible bias, whereas in the second case the bias is noticeable even for small deviations from the true model.     

A note on the universal consistency of the kernel distribution function estimator

The problem of universal consistency of data driven bandwidth selectors for the kernel distribution estimator is analyzed. We provide a uniform in bandwidth result for the kernel estimate of a continuous distribution function. Our smoothness assumption is minimal in the sense that if the true distribution function has some discontinuity then the kernel estimate is no longer consistent.     

非均匀随机数产生

本文详细介绍了产生非均匀随机数的一般方法,常用连续分布(正态分布,Gamma分布,Beta分布,χ2分布和F分布)的抽样法及利用R软件产生随机数的方法。     

Hypothesis testing for the generalized multivariate modified Bessel model

In this paper, we consider hypothesis testing problems in which the involved samples are drawn from generalized multivariate modified Bessel populations. This is a much more general distribution that includes both the multivariate normal and multivariate-t distributions as special cases. We derive the distribution of the Hotelling's T²-statistic for both the one- and two-sample problems, as well as the distribution of the Scheffe's T²-statistic for the Behrens–Fisher problem. In all cases, the non-null distribution of the corresponding F-statistic follows a new distribution which we introduce as the non-central F-Bessel distribution. Some statistical properties of this distribution are studied. Furthermore, this distribution was utilized to perform some power calculations for tests of means for different models which are special cases of the generalized multivariate modified Bessel distribution, and the results compared with those obtained under the multivariate normal case. Under the null hypothesis, however, the non-central F-Bessel distribution reduces to the central F-distribution obtained under the classical normal model.     

虚拟企业收益分配问题博弈研究

收益分配是虚拟企业的一个敏感而关键的问题，制订合理的收益分配方案对虚拟企业无疑是重要的。本分析了虚拟企业收益分配过程应考虑的因素，在此基础上应用博弈论建立了虚拟企业收益分配的博弈模型，并进行了相关分析，所得出的结论可用于虚拟企业的收益分配策略的制订，最后，论证了在虚拟企业中进行合作机制设计和应用的必要性。