Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning
暫譯: 基於排名的方法進行收縮與選擇：應用於機器學習

Saleh, A. K. MD Ehsanes, Arashi, Mohammad, Saleh, Resve a.

Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning
  • 出版商: Wiley
  • 出版日期: 2022-04-12
  • 售價: $4,810
  • 貴賓價: 9.5 折 $4,570
  • 語言: 英文
  • 頁數: 480
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1119625394
  • ISBN-13: 9781119625391
  • 相關分類: Machine Learning

    • 海外代購書籍(需單獨結帳)

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商品描述

Rank-Based Methods for Shrinkage and Selection

A practical and hands-on guide to the theory and methodology of statistical estimation based on rank

Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students.

Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes:

  • Development of rank theory and application of shrinkage and selection
  • Methodology for robust data science using penalized rank estimators
  • Theory and methods of penalized rank dispersion for ridge, LASSO and Enet
  • Topics include Liu regression, high-dimension, and AR(p)
  • Novel rank-based logistic regression and neural networks
  • Problem sets include R code to demonstrate its use in machine learning

商品描述(中文翻譯)

**基於排名的收縮與選擇方法**

**一本關於基於排名的統計估計理論與方法的實用指南**

穩健統計學是當代數學和應用統計方法中的一個重要領域。《基於排名的收縮與選擇方法：應用於機器學習》描述了在收縮和子集選擇中產生更高質量數據分析的技術，以獲得無異常值預測的簡約模型。本書適合統計學家、經濟學家、生物統計學家、數據科學家及研究生。

《基於排名的收縮與選擇方法》詳細闡述了基於排名的理論及其在機器學習中的應用，以增強最小二乘法的穩健性。它還包括：

- 排名理論的發展及收縮與選擇的應用
- 使用懲罰性排名估計器的穩健數據科學方法論
- 懲罰性排名離散度的理論與方法，適用於脊迴歸（ridge）、LASSO 和 Enet
- 主題包括劉回歸（Liu regression）、高維度及自回歸模型 AR(p)
- 新穎的基於排名的邏輯回歸和神經網絡
- 問題集包括 R 語言代碼，以展示其在機器學習中的應用

作者簡介

A. K. Md. Ehsanes Saleh, PhD, is a Professor Emeritus and Distinguished Professor in the School of Mathematics and Statistics, Carleton University, Ottawa, Canada. He is Fellow of IMS, ASA and Honorary member of SSC, Canada.

Mohammad Arashi, PhD, is an Associate Professor at Ferdowsi University of Mashhad in Iran and Extraordinary Professor and C2 rated researcher at University of Pretoria, Pretoria, South Africa. He is an elected member of ISI.

Resve A. Saleh, M.Sc, PhD (Berkeley), is a Professor Emeritus in the Department of ECE at the University of British Columbia, Vancouver, Canada, and formerly with University of Illinois and Stanford University. He is the author of 4 books and Fellow of the IEEE.

Mina Norouzirad, PhD, is a post-doctoral researcher at the Center for Mathematics and Applications (CMA) of NOVA University of Lisbon, Portugal.

作者簡介(中文翻譯)

A. K. Md. Ehsanes Saleh, PhD, 是加拿大渥太華卡爾頓大學數學與統計學院的名譽教授及傑出教授。他是IMS、ASA的會士，以及加拿大統計學會的榮譽會員。

Mohammad Arashi, PhD, 是伊朗法爾多西大學的副教授，同時也是南非比勒陀利亞大學的特聘教授及C2級研究員。他是ISI的當選會員。

Resve A. Saleh, M.Sc, PhD (Berkeley), 是加拿大不列顛哥倫比亞大學電子與計算機工程系的名譽教授，曾任教於伊利諾伊大學和史丹佛大學。他是四本書的作者，也是IEEE的會士。

Mina Norouzirad, PhD, 是葡萄牙里斯本NOVA大學數學與應用中心的博士後研究員。

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