On nonparametric density estimation for multivariate linear long-memory processes

被引：3

作者：

Beran, Jan ^{[1
]}

Telkmann, Klaus ^{[1
]}

机构：

[1] Univ Konstanz, Dept Math & Stat, D-78457 Constance, Germany

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2018年 / 47卷 / 22期

关键词：

Kernel density estimation; Linear process; Long-range dependence; Multivariate; Multivariate time series; 62G07; 62H12; 62M09; 62M10; RANGE DEPENDENT SEQUENCES; VERVAAT ERROR PROCESSES; BANDWIDTH SELECTION; EMPIRICAL PROCESS; WEAK-CONVERGENCE; BAHADUR-KIEFER; LIMIT-THEOREMS; PRINCIPLES;

D O I：

10.1080/03610926.2017.1395048

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：

We consider nonparametric estimation of the density function and its derivatives for multivariate linear processes with long-range dependence. In a first step, the asymptotic distribution of the multivariate empirical process is derived. In a second step, the asymptotic distribution of kernel density estimators and their derivatives is obtained.

引用

页码：5460 / 5473

页数：14

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