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Regression

Models, Methods and Applications

  • Ludwig Fahrmeir
  • Thomas Kneib
  • Stefan Lang
  • Brian Marx

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 1-19
  3. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 21-72
  4. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 73-175
  5. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 177-267
  6. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 269-324
  7. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 325-347
  8. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 349-412
  9. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 413-533
  10. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 535-595
  11. Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx
    Pages 597-620
  12. Back Matter
    Pages 621-698

About this book

Introduction

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference.

Keywords

generalized linear models linear regression mixed models semiparametric regression spatial regression

Authors and affiliations

  • Ludwig Fahrmeir
    • 1
  • Thomas Kneib
    • 2
  • Stefan Lang
    • 3
  • Brian Marx
    • 4
  1. 1.Munich, Department of StatisticsUniversity of MunichMunichGermany
  2. 2.Göttingen, Chair of StatisticsUniversity of GöttingenGöttingenGermany
  3. 3.Innsbruck, Department of StatisticsUniversity of InnsbruckInnsbruckAustria
  4. 4.Experimental StatisticsLouisiana State UniversityBaton RougeUSA

Bibliographic information