EM-Based Inference for Cyclostationary Time Series with Missing Observations
Periodically correlated (or cyclostationary) time series are becoming more and more popular in many areas (see Gardner et al. 2006). However, in many practical situations data that can be modeled with such time series is incomplete. Some preliminary results on that problem have been presented in a previous work by Drake et al. (2013) by the authors. In this chapter we propose a new ECM-type algorithm based on conditional likelihood and profile likelihood to extend estimation to the case when observations are missing completely at random.
KeywordsMean Square Error Profile Likelihood Full Likelihood Conditional Maximum Likelihood Lower Mean Square Error
The chapter was written while Oskar Knapik was visiting the Department of Statistics at University of California at Davis, USA. Its kind hospitality is greatly appreciated. Oskar Knapik gratefully acknowledges the Kosciuszko Foundation for financial support for this research.
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