On the construction of stationary AR(1) models via random distributions

被引：5

作者：

Contreras-Cristan, Alberto ^{[1
]}

Mena, Ramses H. ^{[1
]}

Walker, Stephen G. ^{[2
]}

机构：

[1] Univ Nacl Autonoma Mexico, Inst Invest Matemat Aplicadas & Sistemas, Mexico City 04510, DF, Mexico

[2] Univ Kent, Inst Math Stat & Actuarial Sci, Canterbury, Kent, England

来源：

STATISTICS | 2009年 / 43卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

AR model; Beta-Stacy process; Bayesian non-parametrics; discrete-valued time series; Polya trees; stationary process; BAYESIAN NONPARAMETRIC APPROACH; VARIATE TIME-SERIES; AUTOREGRESSIVE MODELS; POLYA TREES; MIXTURES;

D O I：

10.1080/02331880802259391

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：

We explore a method for constructing first-order stationary autoregressive-type models with given marginal distributions. We impose the underlying dependence structure in the model using Bayesian non-parametric predictive distributions. This approach allows for nonlinear dependency and at the same time works for any choice of marginal distribution. In particular, we look at the case of discrete-valued models; that is the marginal distributions are supported on the non-negative integers.

引用

页码：227 / 240

页数：14

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