All of Regression

A comprehensive and modern introduction to regression and related methods covered with concision and clarity.

This concise modern introduction to regression covers a wide range of topics and contemporary applications, including high-dimensional regression and causal inference, with concision and clarity. Suitable for advanced undergraduate and beginning graduate students, it will also appeal to researchers and practitioners in machine learning and AI.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.