This text provides a modern introduction to regression and classification with an emphasis on big data and R. The main body uses math stat sparingly and always in the context of something concrete; readers can skip the math stat content entirely if they wish.
This text provides a modern introduction to regression and classification with an emphasis on big data and R. The main body uses math stat sparingly and always in the context of something concrete; readers can skip the math stat content entirely if they wish.
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.
Inhaltsangabe
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.
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.
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Shop der buecher.de GmbH & Co. KG Bürgermeister-Wegele-Str. 12, 86167 Augsburg Amtsgericht Augsburg HRA 13309
