Statistical Regression and Classification: From Linear Models to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)

Norman Matloff

  • 出版商: Chapman and Hall/CRC
  • 出版日期: 2017-08-01
  • 售價: $2,800
  • 貴賓價: 9.5$2,660
  • 語言: 英文
  • 頁數: 528
  • 裝訂: Paperback
  • ISBN: 1498710913
  • ISBN-13: 9781498710916
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)


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.



- 全面介紹經典的線性模型和廣義線性模型,並附有機器學習方法的入門材料。
- 鑑於分類在當今許多應用中的重要性,本書詳細介紹了分類問題,特別是多類別情況。
- 鑑於現代數據集的龐大性質,本書還包含一章關於大數據的內容。
- 每章末尾都有特別的數學和計算補充部分,並且練習問題分為數據、數學和補充問題。
- 教師可以根據特定的受眾(如統計學、計算機科學或經濟學專業)調整教學內容。
- 超過75個實際數據的例子。


諾曼·馬特洛夫(Norman Matloff)是加州大學戴維斯分校的計算機科學教授,也是該學院統計學系的創始人之一。他目前的研究重點是推薦系統,以及在小區域估計和觀察研究中應用回歸方法進行偏差減少。他是《統計計算期刊》和《R Journal》的編輯委員會成員。作為一位屢獲殊榮的教師,他還是《R編程藝術》和《數據科學中的並行計算:以R、C++和CUDA為例》的作者。