Overview
- Presents a collection of methodologies formulated and developed in the framework of linear models
- Offers accompanying R code online for the included analyses
- Features several new chapters, as well as new and expanded coverage in this 3rd edition
- Designed to be used independently or in conjunction with the theoretical Plane Answers to Complex Questions
Part of the book series: Springer Texts in Statistics (STS)
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Reviews
“This book is in my opinion a very valuable resource for researchers since it presents the theoretical foundations of linear models in a unified way while discussing a number of applications. … This book is definitely worth considering for anyone looking for an extensive and thorough treatment of advanced topics in linear modeling.” (Fabio Mainardi, MAA Reviews, May 23, 2021)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Advanced Linear Modeling
Book Subtitle: Statistical Learning and Dependent Data
Authors: Ronald Christensen
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-3-030-29164-8
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-29163-1Published: 20 December 2019
Softcover ISBN: 978-3-030-29166-2Published: 08 January 2021
eBook ISBN: 978-3-030-29164-8Published: 20 December 2019
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 3
Number of Pages: XXIII, 608
Number of Illustrations: 70 b/w illustrations, 6 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Computational Mathematics and Numerical Analysis, Statistical Theory and Methods