Linear Regression
Springer International Publishing (Verlag)
978-3-319-55250-7 (ISBN)
This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics.
David Olive is a Professor at Southern Illinois University, Carbondale, IL, USA. His research interests include the development of computationally practical robust multivariate location and dispersion estimators, robust multiple linear regression estimators, and resistant dimension reduction estimators.
Introduction.- Multiple Linear Regression.- Building an MLR Model.- WLS and Generalized Least Squares.- One Way Anova.- The K Way Anova Model.- Block Designs.- Orthogonal Designs.- More on Experimental Designs.- Multivariate Models.- Theory for Linear Models.- Multivariate Linear Regression.- GLMs and GAMs.- Stuff for Students.
"Very nice features of the book are the many practical hints and discussion on how to do model building, the various rules of thumb, the summaries and exercises. ... I think that the book will turn out helpful in particular for people interested in modeling aspects of linear regression with some mathematical background." (Alexander Lindner, zbMath 1417.62002, 2019)
Erscheinungsdatum | 06.05.2017 |
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Zusatzinfo | XIV, 494 p. 57 illus. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Gewicht | 919 g |
Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
Schlagworte | Bootstrap Confidence Region • Experimental Design • Generalized Linear Model • Multivariate regression • Prediction Interval • Regression • Response Transformation • Variable selection |
ISBN-10 | 3-319-55250-3 / 3319552503 |
ISBN-13 | 978-3-319-55250-7 / 9783319552507 |
Zustand | Neuware |
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