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

"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 set...

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Bibliographic Details
Main Authors: Saleh, A. K. Md. Ehsanes (Author), Arashi, M. (Mohammad), 1981- (Author), Saleh, Resve A., 1957- (Author), Norouzirad, Mina (Author)
Format: eBook
Language:English
Published: Hoboken, NJ : John Wiley & Sons, Inc., 2022.
Subjects:
Regression analysis.
Big data.
Machine learning.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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.