Statistical Regression and Classification
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A01=Norman Matloff
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Author_Norman Matloff
automatic-update
Ava
big data analytics
Cart Model
Category1=Non-Fiction
Category=PBT
classification
Conditional Expectation
Convex Hulls
COP=United States
Cran Package
Cumulative Distribution Function
Data Set
Delivery_Delivery within 10-20 working days
eq_isMigrated=2
eq_nobargain
Estimated Class Probabilities
General Maximum Likelihood Estimators
Homoscedasticity Assumption
Language_English
Lasso Estimator
Lasso Idea
Local Linear Smoothing
machine learning
Mathematical Complements Section
model selection techniques
MSE Computation
multivariate analysis
PA=Available
Parallel Coordinates Plot
practical regression applications
Price_€50 to €100
PS=Active
Random Forests
regression
Related Estimation Methods
Ridge Estimator
Ridge Regression
Roc Curve
Shrinkage Estimators
Small Area Estimation
social science statistics
softlaunch
Standard Poisson Regression Model
statistical computing
supervised learning
Variance Bias Tradeoff
Product details
- ISBN 9781498710916
- Weight: 800g
- Dimensions: 156 x 234mm
- Publication Date: 01 Aug 2017
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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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.
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.
