Minimum message length analysis of multiple short time series

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• 作者：Daniel F. Schmidt ; ^{dschmidt@unimelb.edu.au" class="auth_mail" title="E-mail the corresponding author} ; Enes Makalic
• 关键词：Minimum message length ; Bayesian inference ; Nuisance parameters ; Approximate conditional likelihood ; Gaussian autoregressive processes
• 刊名：Statistics & Probability Letters
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：110
• 期：Complete
• 页码：318-328
• 全文大小：414 K

文摘

This paper applies the Bayesian minimum message length principle to the multiple short time series problem, yielding satisfactory estimates for all model parameters as well as a test for autocorrelation. Connections with the method of conditional likelihood are also discussed.