Nonparametric estimation of a maximum of quantiles

被引:0
|
作者
Enss, Georg C. [1 ]
Goetz, Benedict [1 ]
Kohler, Michael [2 ]
Krzyzak, Adam [3 ]
Platz, Roland [4 ]
机构
[1] Tech Univ Darmstadt, Fachgebiet Syst Zuverlassigkeit & Maschinenakust, D-64289 Darmstadt, Germany
[2] Tech Univ Darmstadt, Fachbereich Math, D-64289 Darmstadt, Germany
[3] Concordia Univ, Dept Comp Sci & Software Engn, Montreal, PQ H3G 1M8, Canada
[4] Fraunhofer Inst Struct Durabil & Syst Reliabil LB, D-64289 Darmstadt, Germany
来源
基金
加拿大自然科学与工程研究理事会;
关键词
Conditional quantile estimation; maximal quantile; rate of convergence; supremum norm error; REGRESSION; CONVERGENCE; CONSISTENCY; KERNEL;
D O I
10.1214/14-EJS970
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
A simulation model of a complex system is considered which the outcome is described hy m(p, X), where p is a parameter of the system. X is a random input of the system and in is a real-valued function. The maximum (with respect to p) of the quantiles of m(p, X) is estimated. The quantiles of m(p, X) of a given level are estimated for various values of p from an order statistic of values m(p(i)., X-i) where X, X-1, X-2, ... are independent and identically distributed and where pi is close to p, and the maximal quantile is estimated by the maximum of these quantile estimates. Under assumptions on the smoothness of the function describing the dependency of the values of the quantiles on the parameter p the rate of convergence of this estimate is analyzed. The finite sample size behavior of the estimate is illustrated by simulated data and by applying it in a simulation model of a real mechanical system.
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页码:3176 / 3192
页数:17
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