Greedy Function Approximation: A Gradient Boosting Machine, Jerome H. Friedman, 2001The Annals of Statistics, Vol. 29 (Institute of Mathematical Statistics) - Introduces the Gradient Boosting Machine algorithm, detailing the role of the learning rate (shrinkage) in controlling step size and contributing to regularization.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, 2009 (Springer) - A standard reference text for statistical learning, with a chapter dedicated to boosting that thoroughly explains shrinkage and its regularization effects.
Ensemble Methods: Foundations and Algorithms, Zhi-Hua Zhou, 2012 (Chapman and Hall/CRC)DOI: 10.1201/b12196 - Provides a detailed academic review of ensemble methods, including boosting, with coverage of the shrinkage parameter and its impact on model generalization.