Statistical Learning and Dependent Data
The third edition of this companion volume to Ronald Christensen's work expands on linear modeling concepts to cover Statistical Learning and Dependent Data. It includes new content on nonparametric regression, penalized estimation, and various linear models. R code for analyses is available online, making it a comprehensive resource.
Linear Modeling for Unbalanced Data, Second Edition
The second edition delves into modeling unbalanced data, introducing new chapters on logistic regression, log-linear models, and time-to-event data. It emphasizes modeling main effects and interactions while incorporating advanced techniques such as nonparametric, lasso, and generalized additive regression models. The text also offers a thorough analysis of small unbalanced datasets, making it a comprehensive resource for understanding complex statistical modeling.
This textbook offers a comprehensive introduction to linear models for data analysis, focusing on variance and regression models. It includes numerous exercises, covering key topics like estimation, ANOVA, and experimental design, along with advanced subjects such as split plot models and diagnostics. The new edition features updated discussions on estimation alternatives and variable selection.