Analytic Convergence Rates and Parameterization Issues for the Gibbs Sampler Applied to State Space Models

DOI10.1111/1467-9892.00126zbMath0924.62093OpenAlexW2153637929MaRDI QIDQ4258766

Michael K. Pitt, Neil Shephard

Publication date: 14 September 1999

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/1467-9892.00126

zbMATH Keywords

convergence ratesstochastic volatilityMarkov chain Monte CarloblockingparameterizationGibbs samplingsimulation smoother

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Monte Carlo methods (65C05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

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