Stationary mixture transition distribution (MTD) models via predictive distributions

DOI10.1016/J.JSPI.2006.05.018zbMath1114.62091OpenAlexW2052061543MaRDI QIDQ997299

Ramsés H. Mena, Stephen G. Walker

Publication date: 23 July 2007

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2006.05.018

zbMATH Keywords

Bayesian nonparametricsstationary processrandom probability measureAR modelMTD models

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Stationary stochastic processes (60G10)

Related Items (2)

On Construction and Estimation of Stationary Mixture Transition Distribution Models ⋮ Multivariate normal mean–variance mixture distribution based on Birnbaum–Saunders distribution

Cites Work

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• A Bayesian analysis of some nonparametric problems
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Modeling Flat Stretches, Bursts, and Outliers in Time Series Using Mixture Transition Distribution Models
• Constructing First Order Stationary Autoregressive Models via Latent Processes
• Finite mixture models
• Theory & Methods: Non‐Gaussian Conditional Linear AR(1) Models
• Stationary Autoregressive Models via a Bayesian Nonparametric Approach
• Hierarchical Mixture Modeling With Normalized Inverse-Gaussian Priors

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