Nonparametric and Semiparametric Models

  • Wolfgang Härdle
  • Axel Werwatz
  • Marlene Müller
  • Stefan Sperlich

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

Table of contents

  1. Front Matter
    Pages I-XXVII
  2. Introduction

    1. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 1-18
  3. Nonparametric Models

    1. Front Matter
      Pages 19-19
    2. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 21-38
    3. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 39-83
    4. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 85-141
  4. Semiparametric Models

    1. Front Matter
      Pages 143-143
    2. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 145-165
    3. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 167-188
    4. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 189-209
    5. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 211-251
    6. Wolfgang Härdle, Axel Werwatz, Marlene Müller, Stefan Sperlich
      Pages 253-277
  5. Back Matter
    Pages 279-300

About this book

Introduction

The concept of nonparametric smoothing is a central idea in statistics that aims to simultaneously estimate and modes the underlying structure. The book considers high dimensional objects, as density functions and regression. The semiparametric modeling technique compromises the two aims, flexibility and simplicity of statistical procedures, by introducing partial parametric components. These components allow to match structural conditions like e.g. linearity in some variables and may be used to model the influence of discrete variables.

The aim of this monograph is to present the statistical and mathematical principles of smoothing with a focus on applicable techniques. The necessary mathematical treatment is easily understandable and a wide variety of interactive smoothing examples are given.

The book does naturally split into two parts: Nonparametric models (histogram, kernel density estimation, nonparametric regression) and semiparametric models (generalized regression, single index models, generalized partial linear models, additive and generalized additive models). The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers.

The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.

Keywords

Additive Models Density Estimation Generalized Partial Linear Models Nonparametric Models Regression Semiparametric Model Semiparametric Models Single Index Models Smoothing econometrics modeling statistics

Authors and affiliations

  • Wolfgang Härdle
    • 1
  • Axel Werwatz
    • 2
  • Marlene Müller
    • 3
  • Stefan Sperlich
    • 4
  1. 1.CASE — Center for Applied Statistics and Economics Wirtschaftswissenschaftliche FakultätHumboldt-Universität zu BerlinBerlinGermany
  2. 2.DIW BerlinBerlinGermany
  3. 3.Fraunhofer ITWMKaiserslauternGermany
  4. 4.Departamento de EconomíaUniversidad Carlos III de MadridGetafe (Madrid)Spain

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-17146-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2004
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-62076-8
  • Online ISBN 978-3-642-17146-8
  • Series Print ISSN 0172-7397
  • About this book