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＜電子ブック＞
Advanced Linear Modeling : Statistical Learning and Dependent Data / by Ronald Christensen
(Springer Texts in Statistics. ISSN:21974136)

版	3rd ed. 2019.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2019
大きさ	XXIII, 608 p. 76 illus., 6 illus. in color : online resource
著者標目	*Christensen, Ronald author SpringerLink (Online service)
件　名	LCSH:Probabilities LCSH:Mathematics—Data processing LCSH:Statistics  FREE:Probability Theory FREE:Computational Mathematics and Numerical Analysis FREE:Statistical Theory and Methods
一般注記	1. Nonparametric Regression -- 2. Penalized Estimation -- 3. Reproducing Kernel Hilbert Spaces -- 4. Covariance Parameter Estimation -- 5. Mixed Models and Variance Components -- 6. Frequency Analysis of Time Series -- 7. Time Domain Analysis -- 8. Linear Models for Spacial Data: Kriging -- 9. Multivariate Linear Models: General. 10. Multivariate Linear Models: Applications -- 11. Generalized Multivariate Linear Models and Longitudinal Data -- 12. Discrimination and Allocation -- 13. Binary Discrimination and Regression -- 14. Principal Components, Classical Multidimensional Scaling, and Factor Analysis -- A Mathematical Background -- B Best Linear Predictors -- C Residual Maximum Likelihood -- Index -- Author Index 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 HTTP:URL=https://doi.org/10.1007/978-3-030-29164-8

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