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Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer Texts in Statistics) - Hardcover
Synopsis
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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About the Author
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).
From the Back 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.
"About this title" may belong to another edition of this title.
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Advanced Linear Modeling: Statistical Learning and Dependent Data (Springer Texts in Statistics)
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Advanced Linear Modeling
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a collection of methodologies formulated and developed in the framework of linear modelsOffers accompanying R code online for the included analysesFeatures several new chapters, as well as ne. Seller Inventory # 448678026
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 632 pp. Englisch. Seller Inventory # 9783030291631
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Advanced Linear Modeling : Statistical Learning and Dependent Data
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Now in its third edition, this companion volume to Ronald Christensen's PlaneAnswers to Complex Questions uses three fundamental concepts from standard linear model theory-best linear prediction, projections, and Mahalanobis distance- to extendstandard linear modeling into the realms of Statistical Learning and Dependent Data. This new edition features a wealth of new and revised content. In StatisticalLearning 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 linearmodels, discrimination, and dimension reduction. While numerous references toPlane Answers are made throughout the volume, Advanced Linear Modeling can be usedon its own given a solid background in linear models. Accompanying R code for theanalyses is available online. Seller Inventory # 9783030291631
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