Statistical Regression and Classification

From Linear Models to Machine Learning
1. Auflage 2017
ISBN: 978-1-4987-1091-6
Verlag: CRC Press

Buch, Englisch, 532 Seiten, Format (B × H): 154 mm x 233 mm, Gewicht: 804 g

Reihe: Chapman & Hall/CRC Texts in Statistical Science

This text provides a modern introduction to regression and classification with an emphasis on big data and R. Each chapter is partitioned into a main body section and an extras section. The main body uses math stat very sparingly and always in the context of something concrete, which means that readers can skip the math stat content entirely if they wish. The extras section is for those who feel comfortable with analysis using math stat.

Zielgruppe

This book is intended for professionals and students in statistics, data science, business analytics, biotech, finance, and political science. It also would be useful as a graduate text.

Autoren/Hrsg.

Matloff, Norman

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsstatistik, Demographie
• Mathematik | Informatik Mathematik Stochastik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

Introduction. Linear Regression Models. Generalized Linear Models. Nonparametric Models. Model Parsimony. Use of Regression for Understanding. Large Data. Miscellaneous Topics. Appendix: Quick R. Appendix: Math Stat. Appendix: Matrix Algebra.

Über Autor(innen)

Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. Statistical Regression and Classification: From Linear Models to Machine Learning was awarded the 2017 Ziegel Award for the best book reviewed in Technometrics in 2017. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA.