Advanced Linear Modeling

Statistical Learning and Dependent Data
Third Auflage 2019
ISBN: 978-3-030-29166-2
Verlag: Springer International Publishing

Buch, Englisch, 608 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 943 g

Reihe: Springer Texts in Statistics

Now in its third edition, this companion volume to Ronald Christensen’s 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  are made throughout the volume, can be used on its own given a solid background in linear models.  Accompanying R code for the analyses is available online.

Zielgruppe

Graduate

Autoren/Hrsg.

Christensen, Ronald

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik

Inhaltsverzeichnis

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.

Über Autor(innen)

Ronald Christensen is a Professor of Statistics at the University of New Mexico, Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics, former Chair of the ASA Section on Bayesian Statistical Science and former Editor of . His book publications include Plane Answers to Complex Questions (Springer 2011), Log-Linear Models and Logistic Regression (Springer 1997), Analysis of Variance, Design, and Regression (1996, 2016), and  (2010, with Johnson, Branscum and Hanson).