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arXiv 1707.08065math.PR
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关于独立分量随机向量的多元记录

On Multivariate Records from Random Vectors with Independent Components

M. Falk, A. Khorrami, S. A. Padoan

摘要:

设$\boldsymbol{X}_1,\boldsymbol{X}_2,\dots$是随机向量$\boldsymbol{X}$的独立副本,其值在$\mathbb{R}^d$中,并且具有连续分布函数。随机向量$\boldsymbol{X}_n$是一个完整的记录,如果它的每个组件是一个记录。由于我们要求$\boldsymbol{X}$具有独立的分量,因此单变量记录的关键结果明显地延续。但也有实质性的区别:虽然在$d=1$情况下有无限多个记录,但在$d\geq 2$情况下,序列中只会出现1000个记录。因此,存在概率为1的终端完全记录。我们计算了完整记录的随机总数的分布,并研究了终端记录的分布。对于完整记录,等待时间序列形成马尔可夫链,但与单变量情况不同,现在状态无穷大是状态空间的吸收元素。

英文摘要:

Let $\boldsymbol{X}_1,\boldsymbol{X}_2,\dots$ be independent copies of a random vector $\boldsymbol{X}$ with values in $\mathbb{R}^d$ and with a continuous distribution function. The random vector $\boldsymbol{X}_n$ is a complete record, if each of its components is a record. As we require $\boldsymbol{X}$ to have independent components, crucial results for univariate records clearly carry over. But there are substantial differences as well: While there are infinitely many records in case $d=1$, there occur only finitely many in the series if $d\geq 2$. Consequently, there is a terminal complete record with probability one. We compute the distribution of the random total number of complete records and investigate the distribution of the terminal record. For complete records, the sequence of waiting times forms a Markov chain, but differently from the univariate case, now the state infinity is an absorbing element of the state space.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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