Symbolic interval-valued data analysis for time series based on auto-interval-regressive models

被引：8

作者：

Lin, Liang-Ching ^{[1
]}

Chien, Hsiang-Lin ^{[1
]}

Lee, Sangyeol ^{[2
]}

机构：

[1] Natl Cheng Kung Univ, Inst Data Sci, Dept Stat, Tainan, Taiwan

[2] Seoul Natl Univ, Dept Stat, Seoul 08826, South Korea

来源：

STATISTICAL METHODS AND APPLICATIONS | 2021年 / 30卷 / 01期

基金：

新加坡国家研究基金会;

关键词：

AIR model; HVAIR model; Interval-valued time series; Order statistics; Symbolic data analysis; STATISTICS;

D O I：

10.1007/s10260-020-00525-7

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：

This study considers interval-valued time series data. To characterize such data, we propose an auto-interval-regressive (AIR) model using the order statistics from normal distributions. Furthermore, to better capture the heteroscedasticity in volatility, we design a heteroscedastic volatility AIR (HVAIR) model. We derive the likelihood functions of the AIR and HVAIR models to obtain the maximum likelihood estimator. Monte Carlo simulations are then conducted to evaluate our methods of estimation and confirm their validity. A real data example from the S&P 500 Index is used to demonstrate our method.

引用

页码：295 / 315

页数：21

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