Statistical Learning with Sparsity: The Lasso and Generalizations (Paperback)
暫譯: 稀疏統計學習：Lasso 與其一般化 (平裝本)

Hastie, Trevor, Tibshirani, Robert, Wainwright, Martin

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

買這商品的人也買了...

相關主題

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量的非零參數或權重，因此比密集模型更容易估計和解釋。《統計學習與稀疏性：Lasso及其推廣》介紹了利用稀疏性來幫助恢復數據集中潛在信號的方法。

在這個快速發展的領域中，頂尖專家們描述了線性回歸的Lasso及其計算的簡單坐標下降算法。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、貝葉斯方法和最近開發的方法。此外，本書還探討了矩陣分解、稀疏多變量分析、圖形模型和壓縮感知。最後，書中對Lasso的理論結果進行了調查。

在這個大數據時代，對於一個人或物體所測量的特徵數量可能很大，甚至可能超過觀察數量。本書展示了稀疏性假設如何使我們能夠解決這些問題，並從大型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會欣賞這本對稀疏統計建模的全面且最新的處理。

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 是史丹佛大學的約翰·A·奧弗德克統計學教授。在加入史丹佛大學之前，Hastie 教授曾在 AT&T 貝爾實驗室工作，協助開發在 R 計算系統中廣受歡迎的統計建模環境。Hastie 教授以其在應用統計學方面的研究而聞名，特別是在資料挖掘、生物資訊學和機器學習領域。他在這些領域發表了五本書和超過 180 篇研究文章。2014 年，他獲得了以馬努埃爾和卡羅爾·帕岑命名的統計創新獎。他在史丹佛大學獲得博士學位。

Robert Tibshirani 是史丹佛大學統計學系及健康研究與政策系的教授。他著有五本書，合著三本書，並發表了超過 200 篇研究文章。他對複雜數據集的分析做出了重要貢獻，包括套索回歸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，這篇廣泛引用的文章在推動限制駕駛時使用手機的立法方面發揮了作用。Tibshirani 教授於 1996 年獲得了著名的 COPSS 會長獎，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系及電機工程與計算機科學系的教授。Wainwright 教授以其在統計學與計算之間的理論和方法研究而聞名，特別強調高維統計學、機器學習、圖形模型和信息理論。他在這些領域發表了超過 80 篇論文和一本書，於 2014 年獲得 COPSS 會長獎，並在 2014 年的國際數學家大會上擔任分會講者。他在麻省理工學院（MIT）獲得電機工程與計算機科學博士學位。

類似商品