Likelihood-Based Inference in Autoregressive Models with Scaledt-Distributed Innovations by Means of EM-Based Algorithms

DOI10.1080/03610918.2012.695848zbMath1302.62194OpenAlexW2064754171MaRDI QIDQ5299960

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Publication date: 24 June 2013

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.695848

zbMATH Keywords

EM algorithmECM algorithmautoregressive process\(t\)-distributionECME algorithm

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (4)

EM-based algorithms for autoregressive models with t-distributed innovations ⋮ Maximum likelihood estimation in vector autoregressive models with multivariate scaled t-distributed innovations using EM-based algorithms ⋮ Efficient algorithms for robust estimation in autoregressive regression models using Student’stdistribution ⋮ Robust estimation using multivariate t innovations for vector autoregressive models via ECM algorithm

Cites Work

• Time series: theory and methods.
• Time Series Models in Non-Normal Situations: Symmetric Innovations
• Handbook of Time Series Analysis
• Robust Autoregression: Student-t Innovations Using Variational Bayes
• Multi‐variate t Autoregressions: Innovations, Prediction Variances and Exact Likelihood Equations
• Inference in Arch and Garch Models with Heavy-Tailed Errors
• Robust Statistics
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