Local Regression and Likelihood

  • Clive¬†Loader

Part of the Statistics and Computing book series (SCO)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Pages 45-58
  3. Pages 79-100
  4. Pages 177-194
  5. Pages 209-222
  6. Back Matter
    Pages 239-290

About this book

Introduction

Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software.

Keywords

Density Estimation Fitting Likelihood Local Likelihood Local Regression Variance best fit data analysis statistics

Authors and affiliations

  • Clive¬†Loader
    • 1
  1. 1.Lucent TechnologiesMurray HillUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b98858
  • Copyright Information Lucent Technologies 1999
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-98775-0
  • Online ISBN 978-0-387-22732-0
  • Series Print ISSN 1431-8784
  • About this book