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Advanced Linear Modeling

Statistical Learning and Dependent Data

Ronald Christensen author

Format:Paperback

Publisher:Springer Nature Switzerland AG

Published:8th Jan '21

Currently unavailable, and unfortunately no date known when it will be back

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Advanced Linear Modeling cover

Now in its third edition, this companion volume to Ronald Christensen’s Plane Answers to Complex Questions uses three fundamental concepts from standard linear model theory—best linear prediction, projections, and Mahalanobis distance— to extend standard linear modeling into the realms of Statistical Learning and Dependent Data.  
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.

“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)

ISBN: 9783030291662

Dimensions: unknown

Weight: unknown

608 pages

3rd ed. 2019