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Regression estimation and prediction for discrete time processes

Part of the Lecture Notes in Statistics book series (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.

Keywords

Markov Process Regression Estimation Kernel Estimator Regression Estimator Markov Inequality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • D. Bosq
    • 1
  1. 1.Institut de StatistiqueUniversité Pierre et Marie CurieParis cedex 05France

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