Abstract
The construction and study of a nonparametric predictor are the main purpose of this chapter. In practice such a predictor is in general more efficient and more flexible than the predictors based on BOX and JENKINS method, and nearly equivalent if the underlying model is truly linear. This surprising fact will be clarified at the end of the chapter.
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© 1998 Springer Science+Business Media New York
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Bosq, D. (1998). Regression estimation and prediction for discrete time processes. In: Nonparametric Statistics for Stochastic Processes. Lecture Notes in Statistics, vol 110. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1718-3_4
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DOI: https://doi.org/10.1007/978-1-4612-1718-3_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98590-9
Online ISBN: 978-1-4612-1718-3
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