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Advanced linear modeling: statistical learning and dependent data

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 Statistica...

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Detalles Bibliográficos
Autor principal: Christensen, Ronald
Lenguaje:eng
Publicado: Springer 2019
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-29164-8
http://cds.cern.ch/record/2706841
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author Christensen, Ronald
author_facet Christensen, Ronald
author_sort Christensen, Ronald
collection CERN
description 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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spelling cern-27068412021-04-21T18:11:40Zdoi:10.1007/978-3-030-29164-8http://cds.cern.ch/record/2706841engChristensen, RonaldAdvanced linear modeling: statistical learning and dependent dataMathematical Physics and MathematicsNow 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.Springeroai:cds.cern.ch:27068412019
spellingShingle Mathematical Physics and Mathematics
Christensen, Ronald
Advanced linear modeling: statistical learning and dependent data
title Advanced linear modeling: statistical learning and dependent data
title_full Advanced linear modeling: statistical learning and dependent data
title_fullStr Advanced linear modeling: statistical learning and dependent data
title_full_unstemmed Advanced linear modeling: statistical learning and dependent data
title_short Advanced linear modeling: statistical learning and dependent data
title_sort advanced linear modeling: statistical learning and dependent data
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-29164-8
http://cds.cern.ch/record/2706841
work_keys_str_mv AT christensenronald advancedlinearmodelingstatisticallearninganddependentdata