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Rank-based methods for shrinkage and selection :with application to machine learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Rank-based methods for shrinkage and selection :A.K. Md. Ehsanes Saleh ... [et al.].
Reminder of title:	with application to machine learning /
other author:	Saleh, A. K. Md. Ehsanes,
Published:	Hoboken, NJ :John Wiley & Sons, Inc.,2022.
Description:	xxxi, 448 p. :ill. ;24 cm.
Subject:	Regression analysis.
ISBN:	9781119625391 :

Rank-based methods for shrinkage and selection :with application to machine learning /
Rank-based methods for shrinkage and selection :with application to machine learning /A.K. Md. Ehsanes Saleh ... [et al.]. - Hoboken, NJ :John Wiley & Sons, Inc.,2022. - xxxi, 448 p. :ill. ;24 cm.

Includes bibliographical references and indexes.

Introduction to rank-based regression -- Characteristics of rank-based penalty estimators -- Location and simple linear models -- Analysis of variance (ANOVA) -- Seemingly unrelated simple linear models -- Multiple linear regression models -- Partially linear multiple regression model -- Liu regression models -- Autoregressive models -- High-dimensional models -- Rank-based logistic regression -- Rank-based neural networks.

"The purpose of this book is to lay the groundwork for robust data science using rankbased methods. The field of machine learning has not yet fully embraced a class of robust estimators that would address issues that limit the value of least-squares estimation. For example, outliers in data sets may produce misleading results that are not suitable for inference. They can also affect results obtained from penalty estimators. We believe that robust estimators for regression problems are well-suited to data science. This book is intended to provide both practical and mathematical foundations in the study of rank-based methods. It will introduce a number of new ideas and approaches to the practice and theory of robust estimation and encourage readers to pursue further investigation in this field. While the main goal of this book is to provide a rigorous treatment of the subject matter, we begin with some introductory material to build insight and intuition about rank-based regression and penalty estimators, especially for those who are new to the topic and those looking to understand key concepts. To motivate the need for such methods, we will start with a discussion of the median as it is the key to rank-based methods and then build on that concept towards the notion of robust data science"--

ISBN: 9781119625391 :NT$3903

LCCN: 2021042428Subjects--Topical Terms:

181872
Regression analysis.

LC Class. No.: QA278.2 / .R253 2022

Dewey Class. No.: 519.5/36

Rank-based methods for shrinkage and selection :with application to machine learning /
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020 $z 9781119625414 (epdf)
020 $z 9781119625421 (epub)
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050 0 0 $a QA278.2 $b .R253 2022
082 0 0 $a 519.5/36 $2 23/eng/20211105
245 0 0 $a Rank-based methods for shrinkage and selection : $b with application to machine learning / $c A.K. Md. Ehsanes Saleh ... [et al.].
260 $a Hoboken, NJ : $b John Wiley & Sons, Inc., $c 2022.
300 $a xxxi, 448 p. : $b ill. ; $c 24 cm.
504 $a Includes bibliographical references and indexes.
505 0 $a Introduction to rank-based regression -- Characteristics of rank-based penalty estimators -- Location and simple linear models -- Analysis of variance (ANOVA) -- Seemingly unrelated simple linear models -- Multiple linear regression models -- Partially linear multiple regression model -- Liu regression models -- Autoregressive models -- High-dimensional models -- Rank-based logistic regression -- Rank-based neural networks.
520 $a "The purpose of this book is to lay the groundwork for robust data science using rankbased methods. The field of machine learning has not yet fully embraced a class of robust estimators that would address issues that limit the value of least-squares estimation. For example, outliers in data sets may produce misleading results that are not suitable for inference. They can also affect results obtained from penalty estimators. We believe that robust estimators for regression problems are well-suited to data science. This book is intended to provide both practical and mathematical foundations in the study of rank-based methods. It will introduce a number of new ideas and approaches to the practice and theory of robust estimation and encourage readers to pursue further investigation in this field. While the main goal of this book is to provide a rigorous treatment of the subject matter, we begin with some introductory material to build insight and intuition about rank-based regression and penalty estimators, especially for those who are new to the topic and those looking to understand key concepts. To motivate the need for such methods, we will start with a discussion of the median as it is the key to rank-based methods and then build on that concept towards the notion of robust data science"-- $c Provided by publisher.
650 0 $a Regression analysis. $3 181872
650 0 $a Big data. $3 609582
650 0 $a Machine learning. $3 188639
700 1 $a Saleh, A. K. Md. Ehsanes, $e author. $3 829148