Estimation and Testing Under Sparsity: École d'Été de Probabilités de Saint-Flour XLV - 2015 (Lecture Notes in Mathematics)

Sara van de Geer

  • 出版商: Springer
  • 出版日期: 2016-06-29
  • 售價: $2,330
  • 貴賓價: 9.5$2,214
  • 語言: 英文
  • 頁數: 292
  • 裝訂: Paperback
  • ISBN: 3319327739
  • ISBN-13: 9783319327730
  • 海外代購書籍(需單獨結帳)

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商品描述

Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.

商品描述(中文翻譯)

以Lasso方法為起點,本書描述了研究一般損失函數和稀疏誘導正則化所需的主要要素。它還提供了建立置信區間和檢驗的半參數方法。稀疏誘導方法在高維數據分析中被證明非常有用。例子包括Lasso和群體Lasso方法,以及帶有其他範數懲罰的最小二乘法,如核範數。提供的示例包括廣義線性模型、密度估計、矩陣補全和稀疏主成分。每章結束時都有一個問題部分。本書可用作研究生或博士課程的教科書。