LEADER 03776nam 22006495 450 001 9910364955903321 005 20250409122931.0 010 $a9783030291648 010 $a3030291642 024 7 $a10.1007/978-3-030-29164-8 035 $a(CKB)4100000010013751 035 $a(MiAaPQ)EBC6001730 035 $a(DE-He213)978-3-030-29164-8 035 $a(PPN)242818811 035 $a(MiAaPQ)EBC31886987 035 $a(Au-PeEL)EBL31886987 035 $a(OCoLC)1134853614 035 $a(EXLCZ)994100000010013751 100 $a20191220d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Linear Modeling $eStatistical Learning and Dependent Data /$fby Ronald Christensen 205 $a3rd ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (618 pages) $cillustrations 225 1 $aSpringer Texts in Statistics,$x2197-4136 300 $aIncludes index. 311 08$a9783030291631 311 08$a3030291634 327 $a1. 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. 330 $aNow in its third edition, this companion volume to Ronald Christensen?s Plane Answers to Complex Questions uses three fundamental concepts from standard linear model theory?best linear prediction, projections, and Mahalanobis distance? to extend standard linear modeling into the realms of Statistical Learning and Dependent Data. This new edition features a wealth of new and revised content. In Statistical Learning it delves into nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers are made throughout the volume, Advanced Linear Modeling can be used on its own given a solid background in linear models. Accompanying R code for the analyses is available online. 410 0$aSpringer Texts in Statistics,$x2197-4136 606 $aProbabilities 606 $aMathematics$xData processing 606 $aStatistics 606 $aProbability Theory 606 $aComputational Mathematics and Numerical Analysis 606 $aStatistical Theory and Methods 615 0$aProbabilities. 615 0$aMathematics$xData processing. 615 0$aStatistics. 615 14$aProbability Theory. 615 24$aComputational Mathematics and Numerical Analysis. 615 24$aStatistical Theory and Methods. 676 $a519.5 676 $a519.5 700 $aChristensen$b Ronald$4aut$4http://id.loc.gov/vocabulary/relators/aut$066381 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910364955903321 996 $aAdvanced linear modeling$9147953 997 $aUNINA