Statistical Foundations of Data Science

by Jianqing Fan, Runze Li, Cun-Hui Zhang

This book gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. Offering more details on the topics than similar books, it is a valuable reference for researchers involved with model selection, variable selection, machine learning, and risk management. The book can also be used as a text for graduate and senior undergraduate students.

774 pages, Kindle Edition

Published September 20, 2020

Reviews

[Cam · Rating 1 out of 5]

Terrible intuition given. Just equation after equation. Did not like. Did not finish.

[smoh cat · Rating 3 out of 5]

“Things I wish are true but are rarely true in practice. “ This book has various typos and is extremely difficult to read. Nevertheless this is a much needed book so when I do machine learning I’m aware of every assumption I’m making.