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

Autor:	Ramsés H. Mena, Alberto Contreras-Cristán, Stephen G. Walker
Rok vydání:	2009
Předmět:	Statistics and Probability Nonlinear system Stationary process Dependency (UML) Autoregressive model Bayesian probability Econometrics Structure (category theory) Statistical physics Statistics Probability and Uncertainty Marginal distribution Mathematics
Zdroj:	Statistics. 43:227-240
ISSN:	1029-4910 0233-1888
DOI:	10.1080/02331880802259391
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0335457333a7e2e0f86a68d0de2e7a56 https://doi.org/10.1080/02331880802259391 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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