Robert Tibshirani Books
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, known for their influential contributions to statistical learning theory and methods.
Known for: Statistical Learning with Sparsity: The Lasso and Generalizations, The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Books by Robert Tibshirani
Statistical Learning with Sparsity: The Lasso and Generalizations
This book provides a comprehensive treatment of sparse statistical modeling, focusing on the Lasso and its extensions. It covers theoretical foundations, computational algorithms, and practical applic...
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
This influential textbook provides a comprehensive introduction to statistical learning theory and its applications in data mining and prediction. It covers key methods such as linear regression, clas...
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The Birth of Sparsity: Historical Roots and Conceptual Foundation of the Lasso
The story of the Lasso begins with the challenge of high-dimensional statistics. Classical linear regression worked beautifully when the number of observations far exceeded the number of variables, but as scientists began to collect richer data, we soon found ourselves in situations where predictors...
From Statistical Learning with Sparsity: The Lasso and Generalizations
Mathematical Formulation and Geometry of the Lasso
Let us step inside the mathematics. The classical Lasso problem can be formulated as a constrained optimization: minimize the residual sum of squares subject to an upper bound on the L1 norm of the coefficients. Equivalently, one may view it as a penalized optimization, adding the L1 norm times a re...
From Statistical Learning with Sparsity: The Lasso and Generalizations
Linear Methods for Regression: From Least Squares to Modern Extensions
Linear regression is the oldest and perhaps most enduring method in statistical learning. It begins with a simple, powerful idea: that the expected value of a response can be expressed as a linear combination of predictors. This idea is so natural that it seems inevitable — and yet even this foundat...
From The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Classification and Decision Boundaries: From Logistic Regression to Discriminant Analysis
Classification brings a new flavor to learning, where outcomes are labels rather than numeric values. The task is not to estimate a response, but to assign categories based on observed features. Logistic regression emerges as the natural analogue of linear regression for this setting — a model groun...
From The Elements of Statistical Learning: Data Mining, Inference, and Prediction
About Robert Tibshirani
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, known for their influential contributions to statistical learning theory and methods.
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Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, known for their influential contributions to statistical learning theory and methods.
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