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Smoothing Techniques

With Implementation in S

  • Wolfgang Härdle

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Density Smoothing

    1. Front Matter
      Pages 1-1
    2. Wolfgang Härdle
      Pages 3-42
    3. Wolfgang Härdle
      Pages 43-84
    4. Wolfgang Härdle
      Pages 85-89
    5. Wolfgang Härdle
      Pages 90-119
  3. Regression Smoothing

    1. Front Matter
      Pages 121-121
    2. Wolfgang Härdle
      Pages 123-150
    3. Wolfgang Härdle
      Pages 151-172
    4. Wolfgang Härdle
      Pages 173-195
  4. Back Matter
    Pages 197-261

About this book

Introduction

The author has attempted to present a book that provides a non-technical introduction into the area of non-parametric density and regression function estimation. The application of these methods is discussed in terms of the S computing environment. Smoothing in high dimensions faces the problem of data sparseness. A principal feature of smoothing, the averaging of data points in a prescribed neighborhood, is not really practicable in dimensions greater than three if we have just one hundred data points. Additive models provide a way out of this dilemma; but, for their interactiveness and recursiveness, they require highly effective algorithms. For this purpose, the method of WARPing (Weighted Averaging using Rounded Points) is described in great detail.

Keywords

Area Bootstrapping Counting Lemma algorithms approximation average estimator function implementation kernel likelihood selection statistics techniques

Authors and affiliations

  • Wolfgang Härdle
    • 1
  1. 1.Center for Operations Research and EconometricsUniversité Catholique de LouvainLouvain-La-NeuveBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-4432-5
  • Copyright Information Springer-Verlag New York 1991
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8768-1
  • Online ISBN 978-1-4612-4432-5
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
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