01081cam0 2200277 450 E60020006181320200724062300.020100316d1959 |||||ita|0103 baitaITLazioa cura di Elio MiglioriniNapoliR.Pironti e Figli Editori1959190 p.1 c. geogr. f.t.21 cmCollana di bibliografie geografiche delle regioni italiane1In testa al front.: Consiglio nazionale delle ricerche - Comitato per le scienze storiche, filologiche e filosofiche001LAEC000283582001 *Collana di bibliografie geografiche delle regioni italiane1Migliorini, ElioA600200054914070ITUNISOB20200724RICAUNISOBUNISOB910|Coll|16|K16044E600200061813M 102 Monografia moderna SBNM910|Coll|16|K000012Si16044AcquistovittoriniUNISOBUNISOB20100316122150.020200724062246.0SpinosaLazio102068UNISOB03776nam 22006495 450 991036495590332120250409122931.09783030291648303029164210.1007/978-3-030-29164-8(CKB)4100000010013751(MiAaPQ)EBC6001730(DE-He213)978-3-030-29164-8(PPN)242818811(MiAaPQ)EBC31886987(Au-PeEL)EBL31886987(OCoLC)1134853614(EXLCZ)99410000001001375120191220d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Linear Modeling Statistical Learning and Dependent Data /by Ronald Christensen3rd ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (618 pages) illustrationsSpringer Texts in Statistics,2197-4136Includes index.9783030291631 3030291634 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.Springer Texts in Statistics,2197-4136ProbabilitiesMathematicsData processingStatisticsProbability TheoryComputational Mathematics and Numerical AnalysisStatistical Theory and MethodsProbabilities.MathematicsData processing.Statistics.Probability Theory.Computational Mathematics and Numerical Analysis.Statistical Theory and Methods.519.5519.5Christensen Ronaldauthttp://id.loc.gov/vocabulary/relators/aut66381MiAaPQMiAaPQMiAaPQBOOK9910364955903321Advanced linear modeling147953UNINA