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

Libro 91 de 103: Springer Texts in Statistics

Christensen, Ronald

 
9783030291631: Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer Texts in Statistics)
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ISBN 10: 3030291634 ISBN 13: 9783030291631
Editorial: Springer, 2019

Sinopsis

This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis. This second edition adds new material on nonparametric regression, response surface maximization, and longitudinal models. The book provides a unified approach to these disparate subjects and serves as a self-contained companion volume to the author’s Plane Answers to Complex Questions: The Theory of Linear Models. Ronald Christensen is Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of linear models having linear structure.

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Acerca del 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).

De la contraportada

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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Editorial
Springer
Año de publicación
2019
Idioma
Inglés
ISBN 10 
3030291634
ISBN 13 
9783030291631
Encuadernación
Tapa dura
Número de edición
3
Número de páginas
634
Contacto del fabricante
ProductSafety@springernature.com
ProductSafety@springernature.com

Poststr. 9
Darmstadt
64293
Alemania

Otras ediciones populares con el mismo título

9783030291662: Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer Texts in Statistics)

Edición Destacada

ISBN 10:  3030291669 ISBN 13:  9783030291662
Editorial: Springer, 2021
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