Constructing first order stationary autoregressive models via latent processes

被引：37

作者：

Pitt, MK

Chatfield, C

Walker, SG ^{[1
]}

机构：

[1] Univ Bath, Sch Math Sci, Bath BA2 7AY, Avon, England

[2] Univ Warwick, Coventry CV4 7AL, W Midlands, England

来源：

SCANDINAVIAN JOURNAL OF STATISTICS | 2002年 / 29卷 / 04期

关键词：

autocorrelation function; autoregressive process; EM algorithm; exponential family; latent process; stationary time series;

D O I：

10.1111/1467-9469.00311

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：

First order stationary autoregressive (AR(1)) models are introduced for which there exists a linear relation between the expectations of the observations, and where it is readily possible to arrange the marginal distributions to be other than normal.

引用

页码：657 / 663

页数：7

共 15 条

[1] Normal inverse Gaussian distributions and stochastic volatility modelling [J].