Dependence among order statistics for time-transformed exponential models

IF 0.7 3区 工程技术 Q4 ENGINEERING, INDUSTRIAL
Subhash Kochar, Fabio L. Spizzichino
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引用次数: 0

Abstract

Abstract Let $(X_{1},\ldots,X_{n})$ be a random vector distributed according to a time-transformed exponential model . This is a special class of exchangeable models, which, in particular, includes multivariate distributions with Schur-constant survival functions. Let for $1\leq i\leq n$ , $X_{i:n}$ denote the corresponding i th-order statistic. We consider the problem of comparing the strength of dependence between any pair of X i ’s with that of the corresponding order statistics. It is in particular proved that for $m=2,\ldots,n$ , the dependence of $X_{2:m}$ on $X_{1:m}$ is more than that of X 2 on X 1 according to more stochastic increasingness (positive monotone regression) order, which in turn implies that $(X_{1:m},X_{2:m})$ is more concordant than $(X_{1},X_{2})$ . It will be interesting to examine whether these results can be extended to other exchangeable models.
时变指数模型阶统计量间的相关性
摘要设$(X_{1},\ldots,X_{n})$是一个随机向量,按照时间变换指数模型进行分布。这是一类特殊的可交换模型,特别地,它包括具有舒尔常数生存函数的多变量分布。对于$1\leq i\leq n$, $X_{i:n}$表示对应的i阶统计量。我们考虑比较任意X i对与相应阶统计量之间的依赖强度的问题。特别证明了对于$m=2,\ldots,n$,按照更随机递增(正单调回归)的顺序,$X_{2:m}$对$X_{1:m}$的依赖性大于x2对x1的依赖性,这意味着$(X_{1:m},X_{2:m})$比$(X_{1},X_{2})$更协调。研究这些结果是否可以推广到其他可交换模型将是一件有趣的事情。
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来源期刊
CiteScore
2.20
自引率
18.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The primary focus of the journal is on stochastic modelling in the physical and engineering sciences, with particular emphasis on queueing theory, reliability theory, inventory theory, simulation, mathematical finance and probabilistic networks and graphs. Papers on analytic properties and related disciplines are also considered, as well as more general papers on applied and computational probability, if appropriate. Readers include academics working in statistics, operations research, computer science, engineering, management science and physical sciences as well as industrial practitioners engaged in telecommunications, computer science, financial engineering, operations research and management science.
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