Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer Texts in Statistics) - Softcover

Buch 95 von 111: Springer Texts in Statistics

Christensen, Ronald

 
9783030291662: Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer Texts in Statistics)
Softcover
ISBN 10: 3030291669 ISBN 13: 9783030291662
Verlag: Springer, 2021

Inhaltsangabe

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.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Ronald Christensen is a Professor of Statistics at the University of New Mexico, Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics, former Chair of the ASA Section on Bayesian Statistical Science and former Editor of The American Statistician. His book publications include Plane Answers to Complex Questions (Springer 2011), Log-Linear Models and Logistic Regression (Springer 1997), Analysis of Variance, Design, and Regression (1996, 2016), and  Bayesian Ideas and Data Analysis (2010, with Johnson, Branscum and Hanson).

Von der hinteren Coverseite

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.

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Verlag
Springer
Erscheinungsdatum
2021
Sprache
Englisch
ISBN 10 
3030291669
ISBN 13 
9783030291662
Einband
Taschenbuch
Auflage
3
Anzahl der Seiten
631
Kontakt zum Hersteller
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Poststr. 9
Darmstadt
64293
Deutschland

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