An analysis of temperature data by using scalar component models

被引：0
|
作者：
Kasap, R [1 ]
机构：
[1] Gazi Univ, Fac Arts & Sci, Dept Stat, TR-06500 Ankara, Turkey
来源：
ENVIRONMETRICS | 1999年 / 10卷 / 05期
关键词：
multivariate time series process; canonical correlation analysis; autoregressive moving average; (ARMA) models; scalar component model (SCM); model specification;
D O I：
暂无
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
In the study of multivariate processes, a framework is needed for describing not only the properties of individual series, but also the possible cross-relationship among the series. For this purpose, the method of canonical correlation analysis is used for treatment of multivariate time series which results in scalar component models (SCMs) already given by Tiao and Tsay (1989, Journal of Business and Economic Statistics, 1, 43-56). For the application of this methodology, temperature data are used which led to some useful empirical results. Copyright (C) 1999 John Wiley & Sons, Ltd.
引用
收藏
页码：625 / 631
页数：7
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