Recursive generalized gamma kernel density estimation for nonnegative dependent data

被引：0

作者：

Khemici, Mohamed ^{[1
]}

Zougab, Nabil ^{[1
]}

Ziane, Yasmina ^{[1
]}

Adjabi, Smail ^{[1
]}

机构：

[1] Univ Bejaia, Fac Exact Sci, Res Unit LaMOS, Bejaia 06000, Algeria

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2024年 / 53卷 / 06期

关键词：

Autoregressive conditional duration model; Generalized gamma kernel; Mixing process; Recursive estimator; Stochastic volatility model;

D O I：

10.1080/03610918.2024.2347930

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：

In this article, we consider the recursive generalized gamma (GG) kernel density estimator (KDE) for nonnegative dependent data from stationary alpha-mixing process. Asymptotic bias, variance and mean integrated squared error are provided. Simulation experiments from an autoregressive conditional duration model and a stochastic volatility model are conducted to compare the recursive GG KDE with non-recursive GG KDE. Two applications are provided for nonnegative time series data.

引用

页码：2976 / 2987

页数：12

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