Markov chain Monte Carlo estimation of autoregressive models with application to metal pollutant concentration in sludge

DOI10.1016/0895-7177(95)00175-2zbMath0836.62064OpenAlexW1991958702MaRDI QIDQ1905841

Yanyan Li

Publication date: 22 January 1996

Published in: Mathematical and Computer Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0895-7177(95)00175-2

zbMATH Keywords

outliersBayesian estimationGibbs samplingMarkov chain Monte Carlo techniquesmissing valuesMetropolisautoregressive time series modelsmetal pollutant concentration

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Survival analysis and censored data (62N99) Probabilistic methods, stochastic differential equations (65C99)

Cites Work

• On the parametrization of autoregressive models by partial autocorrelations
• Sampling-Based Approaches to Calculating Marginal Densities
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Accurate Approximations for Posterior Moments and Marginal Densities
• BAYESIAN ANALYSIS OF AUTOREGRESSIVE TIME SERIES VIA THE GIBBS SAMPLER
• Equation of State Calculations by Fast Computing Machines

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