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Regression estimation and prediction for discrete time processes

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Nonparametric Statistics for Stochastic Processes

Part of the book series: Lecture Notes in Statistics ((LNS,volume 110))

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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Authors and Affiliations

  1. Institut de Statistique, Université Pierre et Marie Curie, 4 Place Jussieu, 75252, Paris cedex 05, France

    D. Bosq

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  1. D. Bosq
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© 1996 Springer-Verlag New York, Inc.

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Bosq, D. (1996). 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/10.1007/978-1-4684-0489-0_4

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  • DOI: https://doi.org/10.1007/978-1-4684-0489-0_4

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94713-6

  • Online ISBN: 978-1-4684-0489-0

  • eBook Packages: Springer Book Archive

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