Regression

Models, Methods and Applications

Authors: Fahrmeir, L., Kneib, Th., Lang, S., Marx, B.

  • Applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application
  • Written in textbook style suitable for students, the material is close to current research on advanced regression analysis
  • Availability of (user-friendly) software is a major criterion for the methods selected and presented
  • Many examples and applications from diverse fields illustrate models and methods
  • Most of the data sets are available via http://www.regressionbook.org/
see more benefits

Buy this book

eBook $69.99
price for USA (gross)
  • ISBN 978-3-642-34333-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $89.95
price for USA
  • ISBN 978-3-642-34332-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $89.95
price for USA
  • ISBN 978-3-642-43376-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
About this Textbook

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.

About the authors

Ludwig Fahrmeir is Professor emeritus at the Department of Statistics at Ludwig-Maximilians-University Munich. From 1995 to 2006 he was speaker of the Collaborative Research Center 'Statistical Analysis of Discrete Data', supported financially by the German National Science Foundation. His main research interests are semiparametric regression, longitudinal data analysis and spatial statistics, with applications ranging from social science and risk management to public health and neuroscience.

Thomas Kneib is Professor for Statistics at Georg August University Göttingen, Germany, where he is speaker of the interdisciplinary Centre for Statistics and a Research Training Group on "Scaling Problems in Statistics". He received his PhD in Statistics at Ludwig-Maximilians-University Munich and, during his PostDoc phase, has been Visiting Professor for Applied Statistics at the University of Ulm and Substitute Professor for Statistics at Georg-August-University Göttingen. From 2009 until 2011 he has been Professor for Applied Statistics at Carl von Ossietzky University Oldenburg. His main research interests include semiparametric regression, spatial statistics and quantile regression.

Stefan Lang is Professor for Applied Statistics at  University of Innsbruck, Austria. He received his PhD at Ludwig-Maximilians-University Munich. From 2005 to 2006 he has been Professor for Statistics at University of Leipzig. He is currently editor of Advances of Statistical Analysis and Associate Editor of Statistical Modelling. His main research interests include semiparametric and spatial regression, multilevel modelling and complex Bayesian models, with applications among others in environmetrics, marketing science, real estate and actuarial science.

Brian D. Marx is a full professor in the Department of Experimental Statisitics at Louisiana State University. His main research interests include P-spline smoothiing, ill-conditioned regression problems, and high-dimensional chemometric applications. He is currently serving as coordinating editor for the journal Statistical Modelling and is past chair of the Statistical Modelling Society.

Reviews

From the book reviews:

“This is a very useful book for researchers, in particular those often faced with data not suited to the classical linear model, and for teachers who wish to motivate good students with an introduction to the wonderful and diverse world of modern statistical modeling. The use of interesting examples and well-thought-out remarks, together with important theory, aid the reader in getting a very good feel for the topics covered.” (Luke A. Prendergast, Mathematical Reviews, June, 2014)

“The book is an excellent resource for a wide range of readers … . more accessible to readers interested in applications of these procedures. … Summing Up: Highly recommended. Students of all levels, researchers/faculty, and professionals.” (D. J. Gougeon, Choice, Vol. 51 (8), April, 2014)

“This is a comprehensive review of various types of theoretical and applied regression models and methodology. … The book provides a strong mathematical base for the understanding of various types of regression models and methodology by integrating theory and practical application. … This is an excellent reference for teachers, students, and researchers in statistics, mathematics, and social, economic, and life sciences.” (Kamesh Sivagnanam, Doody’s Book Reviews, August, 2013)

Table of contents (10 chapters)

  • Introduction

    Fahrmeir, Ludwig (et al.)

    Pages 1-19

  • Regression Models

    Fahrmeir, Ludwig (et al.)

    Pages 21-72

  • The Classical Linear Model

    Fahrmeir, Ludwig (et al.)

    Pages 73-175

  • Extensions of the Classical Linear Model

    Fahrmeir, Ludwig (et al.)

    Pages 177-267

  • Generalized Linear Models

    Fahrmeir, Ludwig (et al.)

    Pages 269-324

Buy this book

eBook $69.99
price for USA (gross)
  • ISBN 978-3-642-34333-9
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $89.95
price for USA
  • ISBN 978-3-642-34332-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $89.95
price for USA
  • ISBN 978-3-642-43376-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Regression
Book Subtitle
Models, Methods and Applications
Authors
Copyright
2013
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-34333-9
DOI
10.1007/978-3-642-34333-9
Hardcover ISBN
978-3-642-34332-2
Softcover ISBN
978-3-642-43376-4
Edition Number
1
Number of Pages
XIV, 698
Topics