Linear Model and Extensions

2024/01/01 by Peng Ding, Ding, Peng · 4 voices · 2 citations
Computer Science · Mathematics · #Algorithm #Artificial intelligence #Calculus (dental) #Code (set theory) #Computer science #Data Analysis with R #Geometry #Heuristic #Linear model #Machine learning #Mathematical economics #Mathematical proof #Mathematics #Programming language #Replicate #Statistics #Theoretical computer science

paper · pdf · doi:10.48550/arxiv.2401.00649

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

I developed the lecture notes based on my ``Linear Model'' course at the University of California, Berkeley over the past ten years. This book provides an intermediate-level introduction to the linear model. It balances rigorous proofs and heuristic arguments. This book provides R code to replicate all simulation studies and case studies.

Cited by

• Weak Identification in Peer Effects Estimation

Discussions

• Ooooooo arxiv.org/abs/2401.00649 [bsky, 31 points, 1 comments]
• Linear Model and Extensions [hn, 2 points, 0 comments]
• Linear Models in #MachineLearning and Statistical Learning by Peng Ding: arxiv.org/abs/2401.00649 #statistics #datascience #mathematics [bsky, 0 points, 0 comments]
• This looks like quite a comprehensive overview of linear models! arxiv.org/abs/2401.00649 [bsky, 0 points, 0 comments]

Related

• A rigorous introduction to linear models
• Some Technical Thoughts on Modeling
• The Coordinate-Free Approach to Linear Models
• Linear models and linear mixed effects models in R with linguistic applications
• modelbased: An R package to make the most out of your statistical models through marginal means, marginal effects, and model predictions