Robust estimation for zero-inflated poisson autoregressive models based on density power divergence

被引：18

作者：

Kim, Byungsoo ^{[1
]}

Lee, Sangyeol ^{[2
]}

机构：

[1] Yeungnam Univ, Dept Stat, Gyongsan, South Korea

[2] Seoul Natl Univ, Dept Stat, Seoul, South Korea

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2017年 / 87卷 / 15期

基金：

新加坡国家研究基金会;

关键词：

Robust estimation; minimum density power divergence estimator; zero-inflated Poisson autoregressive model; integer-valued GARCH model; HELLINGER DISTANCE ESTIMATION; PARAMETER CHANGE TEST; TIME-SERIES; MINIMUM; DISTRIBUTIONS; REGRESSION;

D O I：

10.1080/00949655.2017.1351563

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this study, we consider a robust estimation for zero-inflated Poisson autoregressive models using the minimum density power divergence estimator designed by Basu et al. [Robust and efficient estimation by minimising a density power divergence. Biometrika. 1998;85:549-559]. We show that under some regularity conditions, the proposed estimator is strongly consistent and asymptotically normal. The performance of the estimator is evaluated through Monte Carlo simulations. A real data analysis using New South Wales crime data is also provided for illustration.

引用

页码：2981 / 2996

页数：16

共 32 条

[1]

Al-Osh M. A., 1987, Journal of Time Series Analysis, V8, P261, DOI [10.1111/j.1467-9892.1987.tb00438.x, DOI 10.1111/JTSA.1987.8.ISSUE-3]

[2] MINIMUM DISPARITY ESTIMATION FOR CONTINUOUS MODELS - EFFICIENCY, DISTRIBUTIONS AND ROBUSTNESS [J].