Minimum message length analysis of multiple short time series

作者：Schmidt Daniel F^*; Makalic Enes

来源：Statistics & Probability Letters, 2016, 110: 318-328.

DOI：10.1016/j.spl.2015.09.021

• Minimum message length
• Bayesian inference
• Nuisance parameters
• Approximate conditional likelihood
• Gaussian autoregressive processes

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.

• 出版日期2016-3