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- W2026525708 abstract "Abstract We show how to define and then compute efficiently the marginal likelihood of an ARIMA model with missing observations. The computation is carried out by using the univariate version of the modified Kalman filter introduced by Ansley and Kohn (1985a), which allows a partially diffuse initial state vector. We also show how to predict and interpolate missing observations and obtain the mean squared error of the estimate." @default.
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- W2026525708 date "1986-09-01" @default.
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- W2026525708 title "Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data" @default.
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- W2026525708 doi "https://doi.org/10.1080/01621459.1986.10478332" @default.
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