Characterizations and closure under convolution of two classes of multivariate life distributions

Let ?(η) be the class of positive random vectors $\text{T}$ for which min_1?i?nα_iT_i is IFRA (NBU) for all α_i > 0, i = 1,…,n where n is an arbitrary positive integer. Characterizations of the classes ? and η are obtained and utilized to show that η is closed under convolution and that ? is closed under convolution provided one of the two convoluted vectors has independent components.     

On exponential scores statistics for testing against positive aging

It has been shown in this paper that a test proposed by Barlow and Doksum (1972) based on the exponential scores statistic for testing exponentiality against increasing failure rate distributions is consistent for the much wider class of harmonic new better than used in expectation distributions.     

On the Characteristic Properties of Exponential Distribution

New characterizations for the exponential distribution are given in terms of record values and the probabilities of finite sums of independent and identically distributed nonnegative random variables provided that the underlying distribution is either new better than used or new worse than used.     

记录值中的随机序与年龄性质

研究记录值中的随机序与年龄性质．证明了任何通过两个分布函数的行为所定义的随机序,均可以被它们相应的记录值所保持,而两个随机变量之间的剩余财富序,可以导致其相应记录值序列的平均连续增量的序关系．也讨论了K-记录值的连续增量的年龄性质．     

Nonstationary shock models

This paper extends results obtained by Esary, Marshall and Proschan [10]. Life distribution properties of a device subject to shocks governed by a nonhomogeneous Poisson process are related to corresponding properties of the probability of failing after experiencing a given number of shocks. Physically motivated models are analyzed in which shocks cause damage to a set of components, the damages accumulate additively, and when the accumulated damage exceeds a critical threshold (possibly random) for any of the components, the device fails. Bounds are obtained on the moments of the life length of the device.     

Bivariate extreme value distributions based on polynomial dependence functions

In this paper, we are concerned with bivariate differentiable models for joint extremes for dependent data sets. This question is often raised in hydrology and economics when the risk driven by two (or more) factors has to be quantified. Here we give a full characterization of polynomial models by means of their dependence function and dependence measure. Copyright © 2006 John Wiley & Sons, Ltd.     

Assessing the reliability of a nanocomponent using nanocrack growth

In nanoscience and nanotechnology, much attention has been given to the dual problem of designing nanocomponents with novel physical properties and how such nanocomponents can be fabricated. Receiving less attention has been the question of the nanocomponent's reliability; how does a nanocomponent fail and how long does a nanocomponent survive under typical operating conditions? High reliability is necessary to guarantee the advancement and utilization of nanocomponents due to the fact that they account for a high proportion of costs of newly designed nanosystems as well as multiscale systems. A nanocomponent is a component that is made of atoms, and its reliability is determined by these atoms. There are situations where it is hard or impossible to extract information from a nanocomponent about its relationship to its atoms. In this article, we assess the nanocomponent's reliability by using its physical properties. Specifically, it is known that nanocrack growth involves considerable statistical variability and such variability should be accounted for assessing growth. In this paper, we first provide a stochastic nanocrack growth model and then evaluate the reliability of a nanocomponent based on this model. Various properties of this model are obtained. We also evaluate the reliability of a nanocomponent under different assumptions on our proposed growth model. This paper is a modification of the extensive literature on modeling fatigue cracks in materials on a larger scale, applied to nanoscale where growth is not a function of cumulative stress on the component but related to the time to first exceedance of a threshold. Copyright © 2013 John Wiley & Sons, Ltd.     

A representation of bivariate extreme value distributions via norms on \mathbb{R}^{2}

It is known that a bivariate extreme value distribution (EVD) with reverse exponential margins can be represented as , , where is a suitable norm on . We prove in this paper the converse implication, i.e., given an arbitrary norm on , , , defines an EVD with reverse exponential margins, if and only if the norm satisfies for the condition . This result is extended to bivariate EVDs with arbitrary margins as well as to extreme value copulas. By identifying an EVD , , with the unit ball corresponding to the generating norm , we obtain a characterization of the class of EVDs in terms of compact and convex subsets of .     

Reliability properties under a stopping time shift

Considering a series representation of a coherent system using a shift transform of the components lifetime T_i, at its critical level Y_i, we study two problems. First, under such a shift transform, we analyse the preservation properties of the non-parametric distribution classes and secondly the association preserving property of the components lifetime under such transformations.