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In many environmental applications, time series are either incomplete or irregularly spaced. We investigate the application of the Spartan random process to missing data prediction. We employ a novel modified method of moments (MMoM) and the established method of maximum likelihood (ML) for parameter inference. The CPU time of MMoM is shown to be much faster than that of ML estimation and almost independent...
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