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Advanced linear modeling :statistica...

Christensen, Ronald, (1951-)

Advanced linear modeling :statistical learning and dependent data /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Advanced linear modeling :Ronald Christensen.
其他題名:	statistical learning and dependent data /
作者:	Christensen, Ronald,
出版者:	New York :Springer,c2019.
面頁冊數:	xxiii, 608 p. :ill. ;25 cm.
附註:	Previously published as: Advanced linear modeling : multivariate, time series, and spatial data ; nonparametric regression and response surface maximization. New York : Springer, ©2001
標題:	Linear models (Statistics)
ISBN:	9783030291631 :

Advanced linear modeling :statistical learning and dependent data /
Christensen, Ronald,1951-

Advanced linear modeling :statistical learning and dependent data /Ronald Christensen. - 3rd ed. - New York :Springer,c2019. - xxiii, 608 p. :ill. ;25 cm. - Springer texts in statistics. - Springer texts in statistics..

Previously published as: Advanced linear modeling : multivariate, time series, and spatial data ; nonparametric regression and response surface maximization. New York : Springer, ©2001

Includes bibliographical references and indexes.

Nonparametric Regression -- Penalized Estimation -- Reproducing Kernel Hilbert Spaces -- Covariance Parameter Estimation -- Mixed Models and Variance Components -- Frequency Analysis of Time Series -- Time Domain Analysis -- Linear Models for Spatial Data: Kriging -- Multivariate Linear Models: General -- Multivariate Linear Models: Applications -- Generalized Multivariate Linear Models and Longitudinal Data -- Discrimination and Allocation -- Binary Discrimination and Regression -- Principal Components, Classical Multidimensional Scaling, and Factor Analysis.

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.

ISBN: 9783030291631 :EUR$89.99Subjects--Topical Terms:

181866
Linear models (Statistics)

LC Class. No.: QA279 / .C477 2019

Advanced linear modeling :statistical learning and dependent data /
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003 OCoLC
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008 200908s2019 nyua b 001 0 eng d
020 $a 9783030291631 : $c EUR$89.99
020 $z 9783030291648 (electronic book)
021 9 1 $a cam a2200253 a 450
035 $a (OCoLC)1141203564
035 $a on1141203564
040 $a IQU $b eng $c IQU $d IQU $d NTU
050 0 0 $a QA279 $b .C477 2019
072 7 $a QA $2 lcco
100 1 $a Christensen, Ronald, $d 1951- $3 186239
245 1 0 $a Advanced linear modeling : $b statistical learning and dependent data / $c Ronald Christensen.
250 $a 3rd ed.
260 $a New York : $b Springer, $c c2019.
300 $a xxiii, 608 p. : $b ill. ; $c 25 cm.
490 1 $a Springer texts in statistics
500 $a Previously published as: Advanced linear modeling : multivariate, time series, and spatial data ; nonparametric regression and response surface maximization. New York : Springer, ©2001
504 $a Includes bibliographical references and indexes.
505 0 $a Nonparametric Regression -- Penalized Estimation -- Reproducing Kernel Hilbert Spaces -- Covariance Parameter Estimation -- Mixed Models and Variance Components -- Frequency Analysis of Time Series -- Time Domain Analysis -- Linear Models for Spatial Data: Kriging -- Multivariate Linear Models: General -- Multivariate Linear Models: Applications -- Generalized Multivariate Linear Models and Longitudinal Data -- Discrimination and Allocation -- Binary Discrimination and Regression -- Principal Components, Classical Multidimensional Scaling, and Factor Analysis.
520 $a 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.
650 0 $a Linear models (Statistics) $3 181866
830 0 $a Springer texts in statistics. $3 559235