Statistical Learning with Sparsity: The Lasso and Generalizations (Paperback)

Hastie, Trevor, Tibshirani, Robert, Wainwright, Martin

Statistical Learning with Sparsity: The Lasso and Generalizations (Paperback)

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商品描述

Discover New Methods for Dealing with High-Dimensional Data

 

 

 

 

 

 

 

A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.

 

 

 

 

 

 

 

 

 

Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso.

 

 

 

 

 

 

 

 

 

In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

 

 

商品描述(中文翻譯)

發現處理高維數據的新方法

稀疏統計模型只有少數非零參數或權重，因此比密集模型更容易估計和解釋。《稀疏統計學習：Lasso和延伸》介紹了利用稀疏性來幫助恢復一組數據中的潛在信號的方法。

這本書由這個快速發展領域的頂尖專家撰寫，他們描述了線性回歸的Lasso方法以及一種簡單的坐標下降算法來計算它。他們討論了將ℓ1懲罰應用於廣義線性模型和支持向量機，涵蓋了彈性網和群組Lasso等廣義懲罰，並回顧了優化的數值方法。他們還介紹了對擬合（Lasso）模型的統計推斷方法，包括自助法、貝葉斯方法和最近發展的方法。此外，該書還探討了矩陣分解、稀疏多變量分析、圖模型和壓縮感知。最後，該書對Lasso的理論結果進行了綜述。

在這個大數據時代，對於一個人或物體測量的特徵數量可能很大，甚至可能大於觀察數量。這本書展示了稀疏假設如何讓我們應對這些問題，從大數據集中提取有用且可重複的模式。數據分析師、計算機科學家和理論家將欣賞這本全面且最新的稀疏統計建模著作。

作者簡介

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).

 

 

作者簡介(中文翻譯)

Trevor Hastie是斯坦福大學的John A. Overdeck統計學教授。在加入斯坦福大學之前，Hastie教授曾在AT&T貝爾實驗室工作，協助開發了在R計算系統中廣受歡迎的統計建模環境。Hastie教授以應用統計學研究聞名，尤其在數據挖掘、生物信息學和機器學習領域有所貢獻。他在這些領域發表了五本書和180多篇研究文章。2014年，他獲得了Emanuel和Carol Parzen統計創新獎。他在斯坦福大學獲得了博士學位。

Robert Tibshirani是斯坦福大學統計學和健康研究與政策學系的教授。他撰寫了五本書，合著了三本書，並發表了200多篇研究文章。他對複雜數據集分析做出了重要貢獻，包括套索（lasso）和微陣列的顯著性分析（SAM）。他還合著了第一篇將手機使用與車禍聯繫起來的研究，這篇廣受引用的文章在限制駕駛時使用手機的立法中起到了一定作用。Tibshirani教授於1996年獲得了著名的COPSS Presidents' Award，並於2012年當選為美國國家科學院院士。

Martin Wainwright是加州大學伯克利分校統計學和電機工程與計算機科學系的教授。Wainwright教授以統計和計算之間的理論和方法研究聞名，尤其在高維統計、機器學習、圖模型和信息理論方面有著特殊的重點。他在這些領域發表了80多篇論文和一本書，並於2014年獲得了COPSS Presidents' Award，並在2014年的國際數學家大會上擔任分區講師。他在麻省理工學院（MIT）獲得了電機工程和計算機科學的博士學位。

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