Free Book Download: Statistical Learning with Sparsity: The Lasso and Generalizations
We witness an explosion of Big Data in finance, biology, medicine, marketing, and other fields. This book describes the important statistical ideas for learning from large and sparse data in a common conceptual framework.
During the past decade has been an explosion in computation and information technology. With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. This book describes the important ideas in these areas in a common conceptual framework.
Authors: Trevor Hastie, Robert Tibshirani, Martin Wainwright.
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 Introduction
 The Lasso for Linear Models
 Generalized Linear Models
 Generalizations of the Lasso Penalty
 Optimization Methods
 Statistical Inference
 Matrix Decompositions, Approximations, and Completion
 Sparse Multivariate Methods
 Graphs and Model Selection
 Signal Approximation and Compressed Sensing
 Theoretical Results for the Lasso
Trevor Hastie is the John A. Overdeck Professor of Statistics at Stanford University. Prior to joining Stanford University, Professor Hastie worked at AT&T Bell Laboratories, where he helped develop the statistical modeling environment popular in the R computing system. Professor Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics, and machine learning. He has published five books and over 180 research articles in these areas. In 2014, he received the Emanuel and Carol Parzen Prize for Statistical Innovation. He earned a PhD from Stanford University.
Robert Tibshirani is a professor in the Departments of Statistics and Health Research and Policy at Stanford University. He has authored five books, coauthored three books, and published over 200 research articles. He has made important contributions to the analysis of complex datasets, including the lasso and significance analysis of microarrays (SAM). He also coauthored the first study that linked cell phone usage with car accidents, a widely cited article that has played a role in the introduction of legislation that restricts the use of phones while driving. Professor Tibshirani was a recipient of the prestigious COPSS Presidentsâ€™ Award in 1996 and was elected to the National Academy of Sciences in 2012.
Martin Wainwright is a professor in the Department of Statistics and the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Professor Wainwright is known for theoretical and methodological research at the interface between statistics and computation, with particular emphasis on highdimensional statistics, machine learning, graphical models, and information theory. He has published over 80 papers and one book in these areas, received the COPSS Presidentsâ€™ Award in 2014, and was a section lecturer at the International Congress of Mathematicians in 2014. He received PhD in EECS from the Massachusetts Institute of Technology (MIT).
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