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Dimitris N. Politis

Model-Free Prediction and Regression

A Transformation-Based Approach to Inference

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The  Model-Free  Prediction Principle expounded upon in this monograph is based on the simple notion of transforming a complex dataset to one that is easier  to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof, and yields optimal predictors in diverse settings such as regression and time series. Furthermore, the Model-Free Bootstrap takes us beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality.Prediction has been tr… Mehr

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Produktdetails


  • ISBN: 978-3-319-35249-7
  • EAN: 9783319352497
  • Produktnummer: 23924967
  • Verlag: Springer Nature EN
  • Sprache: Englisch
  • Erscheinungsjahr: 2016
  • Seitenangabe: 246 S.
  • Masse: H23.5 cm x B15.5 cm 4'044 g
  • Auflage: Softcover reprint of the original 1st ed. 2015
  • Abbildungen: Tabellen, schwarz-weiss
  • Gewicht: 4044

Über den Autor


Dimitris N. Politis is Professor of Mathematics and Adjunct Professor of Economics at the University of California, San Diego. His research interests include Time Series Analysis, Resampling and Subsampling, Nonparametric Function Estimation, and Model-free Prediction. He has served as Editor of the IMS Bulletin (2010-2013), Co-Editor of the Journal of Time Series Analysis (2013-present), Co-Editor of the Journal of Nonparametric Statistics (2008-2011), and as Associate Editor for several journals including Bernoulli, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society, Series B. He is a fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association, former fellow of the John Simon Guggenheim Memorial Foundation, and co-founder (with M. Akritas and S.N. Lahiri) of the International Society for NonParametric Statistics.

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