Advertisement

Inference for Functional Data with Applications

  • Lajos Horváth
  • Piotr Kokoszka

Part of the Springer Series in Statistics book series (SSS, volume 200)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Lajos Horváth, Piotr Kokoszka
    Pages 1-17
  3. Independent functional observations

    1. Front Matter
      Pages 19-19
    2. Lajos Horváth, Piotr Kokoszka
      Pages 21-36
    3. Lajos Horváth, Piotr Kokoszka
      Pages 37-43
    4. Lajos Horváth, Piotr Kokoszka
      Pages 45-63
    5. Lajos Horváth, Piotr Kokoszka
      Pages 65-77
    6. Lajos Horváth, Piotr Kokoszka
      Pages 79-104
    7. Lajos Horváth, Piotr Kokoszka
      Pages 105-124
  4. The functional linear model

    1. Front Matter
      Pages 125-125
    2. Lajos Horváth, Piotr Kokoszka
      Pages 127-145
    3. Lajos Horváth, Piotr Kokoszka
      Pages 147-167
    4. Lajos Horváth, Piotr Kokoszka
      Pages 169-190
    5. Lajos Horváth, Piotr Kokoszka
      Pages 191-224
    6. Lajos Horváth, Piotr Kokoszka
      Pages 225-232
  5. Dependent functional data

    1. Front Matter
      Pages 233-233
    2. Lajos Horváth, Piotr Kokoszka
      Pages 235-252
    3. Lajos Horváth, Piotr Kokoszka
      Pages 253-276
    4. Lajos Horváth, Piotr Kokoszka
      Pages 277-288
    5. Lajos Horváth, Piotr Kokoszka
      Pages 289-341
    6. Lajos Horváth, Piotr Kokoszka
      Pages 343-374
  6. Back Matter
    Pages 405-422

About this book

Introduction

This book presents recently developed statistical methods and theory required for the application of the tools of functional data analysis to problems arising in geosciences, finance, economics and biology. It is concerned with inference based on second order statistics, especially those related to the functional principal component analysis. While it covers inference for independent and identically distributed functional data, its distinguishing feature is an in depth coverage of dependent functional data structures, including functional time series and spatially indexed functions. Specific inferential problems studied include two sample inference, change point analysis, tests for dependence in data and model residuals and functional prediction. All procedures are described algorithmically, illustrated on simulated and real data sets, and supported by a complete asymptotic theory.

The book can be read at two levels. Readers interested primarily in methodology will find detailed descriptions of the methods and examples of their application. Researchers interested also in mathematical foundations will find carefully developed theory. The organization of the chapters makes it easy for the reader to choose an appropriate focus. The book introduces the requisite, and frequently used, Hilbert space formalism in a systematic manner. This will be useful to graduate or advanced undergraduate students seeking a self-contained introduction to the subject. Advanced researchers will find novel asymptotic arguments.

Keywords

Asymptotic theory Distributed functions Functional data analysis Functional time series Hilbert space theory Regression model

Authors and affiliations

  • Lajos Horváth
    • 1
  • Piotr Kokoszka
    • 2
  1. 1., Department of MathematicsUniversity of UtahSalt Lake CityUSA
  2. 2., Department of StatisticsColorado State UniversityFort CollinsUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-3655-3
  • Copyright Information Springer Science+Business Media New York 2012
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-3654-6
  • Online ISBN 978-1-4614-3655-3
  • Series Print ISSN 0172-7397
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Biotechnology
Finance, Business & Banking
Electronics
Telecommunications
Aerospace
Oil, Gas & Geosciences
Engineering