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Regression

Models, Methods and Applications

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

  • Provides an applied and unified introduction to parametric, nonparametric and semiparametric regression
  • Closes the gap between theory and application, featuring examples and applications, and user-friendly software
  • Features data sets and software online at www.regressionbook.org
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eBook 117,69 €
price for Spain (gross)
  • The eBook version of this title will be available soon
  • Due: October 30, 2021
  • ISBN 978-3-662-63882-8
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover 155,99 €
price for Spain (gross)
  • Due: October 30, 2021
  • ISBN 978-3-662-63881-1
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this Textbook

Now in its second edition, this textbook provides an applied and unified introduction to parametric, nonparametric 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 numerous examples and case studies. The most important definitions and statements are concisely summarized in boxes, and the underlying data sets and code are available online on the book’s dedicated website. Availability of (user-friendly) software has been a major criterion for the methods selected and presented.

The chapters address the classical linear model and its extensions, generalized linear models, categorical regression models, mixed models, nonparametric regression, structured additive regression, quantile regression and distributional regression models. Two appendices describe the required matrix algebra, as well as elements of probability calculus and statistical inference.

In this substantially revised and updated new edition the overview on regression models has been extended, and now includes the relation between regression models and machine learning, additional details on statistical inference in structured additive regression models have been added and a completely reworked chapter augments the presentation of quantile regression with a comprehensive introduction to distributional regression models. Regularization approaches are now more extensively discussed in most chapters of the book.

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 at an intermediate mathematical level and assumes only knowledge of basic probability, calculus, matrix algebra and statistics.

About the authors

Ludwig Fahrmeir is Professor Emeritus at the Institute of Statistics at LMU Munich, Germany. From 1995 to 2006 he was the speaker of the Collaborative Research Center 'Statistical Analysis of Discrete Structures', supported financially by the German National Science Foundation. His main research interests include 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 a Professor of Statistics at the University of Göttingen, Germany, where he is the Speaker of the interdisciplinary Centre for Statistics and Vice-Speaker of the Campus Institute Data Science. He received his PhD in Statistics at LMU Munich and, during his PostDoc phase, was Visiting Professor of Applied Statistics at the University of Ulm and Substitute Professor of Statistics at the University of Göttingen. From 2009 until 2011 he was Professor of Applied Statistics at Carl von Ossietzky University Oldenburg. His main research interests include semiparametric regression, spatial statistics and distributional regression.

Stefan Lang is a Professor of Applied Statistics at the University of Innsbruck, Austria. He received his PhD at LMU Munich. From 2005 to 2006 he was Professor of Statistics at the University of Leipzig. He is currently Associate Editor of the journal Statistical Modelling. His main research interests include semiparametric and spatial regression, multilevel modelling and complex Bayesian models, with applications, among others, in development economics, environmetrics, marketing science, real estate and actuarial science.

Brian D. Marx is Professor at the Department of Experimental Statistics at Louisiana State University, LA, USA. His main research interests include P-spline smoothing, ill-conditioned regression problems, and high-dimensional chemometric applications. He is currently serving as Coordinating Editor for the journal Statistical Modelling, is past chair of the Statistical Modelling Society, and is a Fellow of the American Statistical Association.

Buy this book

eBook 117,69 €
price for Spain (gross)
  • The eBook version of this title will be available soon
  • Due: October 30, 2021
  • ISBN 978-3-662-63882-8
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
Hardcover 155,99 €
price for Spain (gross)
  • Due: October 30, 2021
  • ISBN 978-3-662-63881-1
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Regression
Book Subtitle
Models, Methods and Applications
Authors
Copyright
2021
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag GmbH Germany, part of Springer Nature
eBook ISBN
978-3-662-63882-8
DOI
10.1007/978-3-662-63882-8
Hardcover ISBN
978-3-662-63881-1
Edition Number
2
Number of Pages
XX, 690
Number of Illustrations
197 b/w illustrations, 3 illustrations in colour
Topics