Partially Linear Models

  • Wolfgang Härdle
  • Hua Liang
  • Jiti Gao
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Table of contents

  1. Front Matter
    Pages i-x
  2. Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 1-18
  3. Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 19-44
  4. Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 45-54
  5. Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 55-75
  6. Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 77-126
  7. Wolfgang Härdle, Hua Liang, Jiti Gao
    Pages 127-180
  8. Back Matter
    Pages 181-203

About this book

Introduction

In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.

Keywords

Estimator Measure Regression Resampling Time series data analysis linear regression

Authors and affiliations

  • Wolfgang Härdle
    • 1
  • Hua Liang
    • 2
  • Jiti Gao
    • 3
  1. 1.Institut für Statistik und ÖkonometrieHumboldt-Universität zu BerlinBerlinGermany
  2. 2.Frontier Science & Technology Research FoundationHarvard School of Public HealthCestnut HillUSA
  3. 3.Department of Mathematics and StatisticsThe University of Western AustraliaNedlandsAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-57700-0
  • Copyright Information Physica-Verlag Heidelberg 2000
  • Publisher Name Physica, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-7908-1300-5
  • Online ISBN 978-3-642-57700-0
  • Series Print ISSN 1431-1968
  • About this book