Ronald Christensen

Advanced Linear Modeling (eBook, PDF)

Statistical Learning and Dependent Data

• Format: PDF

Produktbeschreibung

This book will serve as a reference book for graduate students and researchers in statistics. Written by a leading researcher in the field, it discusses advanced topics in the area of linear models.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 608
• Erscheinungstermin: 20. Dezember 2019
• Englisch
• ISBN-13: 9783030291648
• Artikelnr.: 58446473

Autorenporträt

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 The American Statistician. 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 Bayesian Ideas and Data Analysis (2010, with Johnson, Branscum and Hanson).

Inhaltsangabe

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.

Rezensionen

"This book is in my opinion a very valuable resource for researchers since it presents the theoretical foundations of linear models in a unified way while discussing a number of applications. ... This book is definitely worth considering for anyone looking for an extensive and thorough treatment of advanced topics in linear modeling." (Fabio Mainardi, MAA Reviews, May 23, 2021)