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Advanced Linear Modeling

Statistical Learning and Dependent Data

  • Ronald Christensen
Textbook

Part of the Springer Texts in Statistics book series (STS)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Ronald Christensen
    Pages 1-58
  3. Ronald Christensen
    Pages 59-85
  4. Ronald Christensen
    Pages 87-123
  5. Ronald Christensen
    Pages 125-159
  6. Ronald Christensen
    Pages 161-194
  7. Ronald Christensen
    Pages 195-246
  8. Ronald Christensen
    Pages 247-319
  9. Ronald Christensen
    Pages 321-355
  10. Ronald Christensen
    Pages 357-388
  11. Ronald Christensen
    Pages 389-422
  12. Ronald Christensen
    Pages 457-501
  13. Ronald Christensen
    Pages 503-531
  14. Back Matter
    Pages 575-608

About this book

Introduction

Now in its third edition, this companion volume to Ronald Christensen’s Plane Answers to Complex Questions uses three fundamental concepts from standard linear model theory—best linear prediction, projections, and Mahalanobis distance— to extend standard linear modeling into the realms of Statistical Learning and Dependent Data.  

This new edition features a wealth of new and revised content.  In Statistical Learning it delves into nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines.  For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction.  While numerous references to Plane Answers are made throughout the volume, Advanced Linear Modeling can be used on its own given a solid background in linear models.  Accompanying R code for the analyses is available online.

Keywords

ANOVA Excel Factor analysis STATISTICA Time series data analysis mathematical statistics heteroscedasticity mixed models multivariate models

Authors and affiliations

  • Ronald Christensen
    • 1
  1. 1.Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-29164-8
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-29163-1
  • Online ISBN 978-3-030-29164-8
  • Series Print ISSN 1431-875X
  • Series Online ISSN 2197-4136
  • Buy this book on publisher's site
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