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• W1983818306 abstract "This paper develops a state space modeling for long-range dependent data. Although a long-range dependent process has an infinite-dimensional state space representation, it is shown that by using the Kalman filter, the exact likelihood function can be computed recursively in a finite number of steps. Furthermore, an approximation to the likelihood function based on the truncated state space equation is considered. Asymptotic properties of these approximate maximum likelihood estimates are established for a class of long-range dependent models, namely, the fractional autoregressive moving average models. Simulation studies show rapid converging properties of the approximate maximum likelihood approach." @default.
• W1983818306 created "2016-06-24" @default.
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• W1983818306 date "1998-04-01" @default.
• W1983818306 modified "2023-09-23" @default.
• W1983818306 title "State space modeling of long-memory processes" @default.
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• W1983818306 doi "https://doi.org/10.1214/aos/1028144856" @default.
• W1983818306 hasPublicationYear "1998" @default.
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