几何随机变量极值和标记点过程的收敛速度
Rates of convergence for extremes of geometric random variables and marked point processes
摘要:
我们使用Stein-Chen方法来研究一元和二元几何定律极值问题的极值行为。我们得到了i的最大值律收敛到Gumbel分布的速度。I. D.几何随机变量,并表明收敛速度更快时,近似的离散Gumbel。我们同样找到了一个收敛速率的法律的最大值的二元马歇尔-奥尔金几何随机对时,近似的离散极限法。本文引入了以单变量和双变量Marshall-Olkin几何变量为标志的标志点过程(MPPEs),并在适当的概率距离下,给出了MPPEs的规律与具有相同平均测度的泊松过程的规律的近似误差的界.然后,我们近似的另一个泊松过程与一个随机使用的平均测量,并估计这个额外的近似的误差。这项工作包含并扩展了第二作者在Andrew D.巴托
英文摘要:
We use the Stein-Chen method to study the extremal behaviour of the problem of extremes for univariate and bivariate geometric laws. We obtain a rate for the convergence to the Gumbel distribution of the law of the maximum of i. i. d. geometric random variables, and show that convergence is faster when approximating by a discretised Gumbel. We similarly find a rate of convergence for the law of maxima of bivariate Marshall-Olkin geometric random pairs when approximating by a discrete limit law. We introduce marked point processes of exceedances (MPPEs), both with univariate and bivariate Marshall-Olkin geometric variables as marks and we determine bounds on the error of the approximation, in an appropriate probability metric, of the law of the MPPE by that of a Poisson process with same mean measure. We then approximate by another Poisson process with an easier-to-use mean measure and estimate the error of this additional approximation. This work contains and extends results contained in the second author's PhD thesis (available at arXiv:1310.2564) under the supervision of Andrew D. Barbour.
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