JSM 2005 - Toronto

Abstract #303570

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 178
Type: Topic Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303570
Title: Stationary Models via a Bayesian Approach
Author(s): Ramsés H. Mena*+
Companies: IIMAS-UNAM
Address: Depto Probabilidad y Estadstica, Mexico DF, 01000, Mexico
Keywords: Autoregressive models ; Bayesian nonparametrics ; Markov processes ; Stationary process
Abstract:

Stationary models arise in a wide variety of problems where statistical analysis is applied. They naturally occur when considering phenomena that evolve over time and retain certain distributional features. In particular, it is of interest to construct stationary models with marginal distributions belonging to arbitrary, but fixed, parametric families. I introduce an approach to constructing strictly stationary Markovian models with arbitrary stationary distributions and a flexible dependence structure. I use Bayesian predictive distributions, based on single observations, to model the underlying transition distribution. This is a natural and highly flexible way to model Markov processes.

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