Statistical Regression and Classification EBOOK Tooltip From Linear Models to Machine Learning

Auteur: Norman Matloff
Taal: Engels
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  • Engels
  • E-book
  • 9781351645898
  • Druk: 1
  • september 2017
  • Adobe ePub
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Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. 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. 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.


. . . Matloff delivers a well-balanced book for advanced beginners. Besides the mathematical formulas, he also presents many chunks of R code, and if the reader is able to read R code, the formulas and calculations become clearer. Due to the computational R code, the well-written Appendix, and an overall clear English, the book will help students and autodidacts. Matloff has written a textbook of the best kind for such a broad topic. ~ Jochen Kruppa, Biometric Journal . . . the book is well suitable for a wide audience: For practitioners interested in applying the methodology, for students in statistics as well as economics/social sciences and computer science. Even in more mathematically oriented classes it can be used as a complimentary text to the usual theoretic textbooks deepening students ability to interpret and question statistical results. ~ Claudia Kirch, Magdeburg This is an application-oriented book introducing frequently used classification and regression methods and the principles behind them. This book tries to keep a balance between theory and practice. It not only elaborates the theories of statistical regression and classification, but also provides large amount of real world examples and R codes to help the reader practice what they learned. As stated in the preface, the targeted readers are data analysts and college students. The style of the book fits well to the anticipated audience. ~ Quanquan Gu, University of Virginia

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september 2017
Ebook formaat
Adobe ePub


Norman Matloff
Chapman and Hall/CRC

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From Linear Models to Machine Learning
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