PDF Download Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
PDF Download Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
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Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
PDF Download Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
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Review
"The authors study and analyze methods using the sparsity property of some statistical models in order to recover the underlying signal in a dataset. They focus on the Lasso technique as an alternative to the standard least-squares method."―Zentralblatt MATH 1319 "The book includes all the major branches of statistical learning. For each topic, the authors first give a concise introduction of the basic problem, evaluate conventional methods, pointing out their deficiencies, and then introduce a method based on sparsity. Thus, the book has the potential to be the standard textbook on the topic."―Anand Panangadan, California State University, Fullerton "It always first discusses regularized models based on equations, followed by example applications, before ending with a bibliography section detailing the historical development of the given method. Software recommendations (mostly open source R packages) are typically provided either in the main part or bibliography section of each chapter. And each chapter concludes with a set of selected exercises meant to deepen the gained knowledge on the given subject, which of course is of great help for teachers of statistics. For these reasons, we congratulate the authors of Statistical Learning with Sparsity and recommend the book to all statistically-inclined readers from intermediate to expert levels. In addition, it is worth pointing out that even for non-statisticians, the book is able to demonstrate,based on numerous real-world examples, the power of regularization."―Ivan Kondofersky and Fabian J. Theis, Institute for Computational Biology
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About the Author
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, co-authored 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 co-authored 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 Presidents’ 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 high-dimensional statistics, machine learning, graphical models, and information theory. He has published over 80 papers and one book in these areas, received the COPSS Presidents’ 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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Product details
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability (Book 143)
Hardcover: 367 pages
Publisher: Chapman and Hall/CRC; 1 edition (May 7, 2015)
Language: English
ISBN-10: 1498712169
ISBN-13: 978-1498712163
Product Dimensions:
6.2 x 1 x 9.2 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review:
4.8 out of 5 stars
8 customer reviews
Amazon Best Sellers Rank:
#315,578 in Books (See Top 100 in Books)
This book is about bet on sparsity. In Machine Learning, there are plenty of approaches that might work on data of interest. The accent is on cases p>>n and one wants to get more interpretable models.
Very helpful!! Exactly what I need, how I wish I could have found it earlier.
Great book!
Great product, fast delivery, thanks!
Awesome! This book is right on time. But it. Read it.
Hastie and Tibshirani are machine learning superstars and I believe this new resource will play an important role in statistical learning just like their previous texts. The timing is perfect for a deep look at the lasso as big data is placing stringent requirements on how enterprise data assets are being used for strategic advantage.
The statistics/machine learning community has been bombarded with so many variants of LASSO, for so many different types of methodology, without any general, unifying treatment of ths subject. The result is more confusion than insight. This book fills that void, and is sure to be much cited as a reference. It will be quite useful to me.
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