All of Regression
Published on 4. June 2026
Book
Hardback
220 pages
978-1-009-70281-2 (ISBN)
Description
This comprehensive modern look at regression covers a wide range of topics and relevant contemporary applications, going well beyond the topics covered in most introductory books. With concision and clarity, the authors present linear regression, nonparametric regression, classification, logistic and Poisson regression, high-dimensional regression, quantile regression, conformal prediction and causal inference. There are also brief introductions to neural nets, deep learning, random effects, survival analysis, graphical models and time series. Suitable for advanced undergraduate and beginning graduate students, the book will also serve as a useful reference for researchers and practitioners in data science, machine learning, and artificial intelligence who want to understand modern methods for data analysis.
Reviews / Votes
'Remarkable in scope and clarity, this book masterfully traces the evolution of regression from classical linear modeling to its many modern descendants. Each chapter offers a lucid introduction to a distinct methodology, explained with precision and insight from a contemporary perspective. Complete with exercises and datasets, it will serve equally well as a textbook and an indispensable research resource.' Edward I. George, The Wharton School, The University of Pennsylvania 'Like Wasserman's earlier All of ... books, this new text is exceptionally clear and concise, offering broad coverage of an important topic. It's an excellent resource for both teaching and as a handy desk reference highly recommended.' Robert Tibshirani, Stanford UniversityMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Illustrations
Worked examples or Exercises
Dimensions
Height: 250 mm
Width: 175 mm
Thickness: 17 mm
Weight
567 gr
ISBN-13
978-1-009-70281-2 (9781009702812)
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Schweitzer Classification
Persons
Isabella Verdinelli is Professor in Residence in the Department of Statistics and Data Science at Carnegie Mellon University, where she has been affiliated since 1988. She was Professor of Statistics at the University of Rome from 1975 to 2013. She has authored a number of papers on experimental design, Bayesian inference, manifold estimation, clustering, structure recovery and feature importance. Larry Wasserman is the UPMC University Professor of Statistics and Data Science at Carnegie Mellon University. He is also Professor in the Department of Machine Learning. He is a member of the National Academy of Sciences, a Fellow of the Institute of Mathematical Statistics and a Fellow of the American Statistical Association. He is a winner of the Committee of Presidents of Statistical Societies (COPSS) Presidents' Award.
Content
Preface; Notation; 1. Introduction; 2. Linear regression; 3. Prediction error, cross-validation and model selection; 4. High dimensional linear regression; 5. Logistic and Poisson regression; 6. Univariate nonparametric regression; 7. Nonparametric regression with multiple features; 8. Quantile regression; 9. Classification; 10. Prediction sets and conformal inference; 11. Causal inference; 12. Other topics; Appendix A. Matrix theory; Appendix B. Basic probability and statistics; Data Sources; References; Index.