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Robust Likelihood Calculation for Time Series
Ross H. Taplin
Journal of the Royal Statistical Society. Series B (Methodological)
Vol. 55, No. 4 (1993), pp. 829-836
Stable URL: http://www.jstor.org/stable/2345996
Page Count: 8
You can always find the topics here!Topics: Induced substructures, Outliers, Time series, Time series models, Kalman filters, Estimation methods, Statism, Random walk, Maximum likelihood estimation, Statistical estimation
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We propose a computationally efficient method for calculating the likelihoods of a time series under many submodels, each of which assumes a patch of outliers or level shifts. We assume a state space representation of the time series model with a Bayesian-type treatment of anomalies. The calculations form the basis for an efficient and robust estimation procedure. The method is also applicable to linear regression with correlated errors and is illustrated with two examples.
Journal of the Royal Statistical Society. Series B (Methodological) © 1993 Royal Statistical Society