Efficient importance sampling in mixture frameworks

DOI10.1016/j.csda.2013.01.025zbMath1506.62096OpenAlexW1973634505MaRDI QIDQ1623542

Roman Liesenfeld, Tore Selland Kleppe

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.01.025

zbMATH Keywords

mixtureMonte Carloimportance samplingdata augmentationrealized volatilitymarginalized likelihooddynamic latent variable model

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Monte Carlo methods (65C05)

Related Items (2)

Importance Sampling-Based Transport Map Hamiltonian Monte Carlo for Bayesian Hierarchical Models ⋮ Particle efficient importance sampling

Uses Software

• AdMit

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