Rank-Based Methods for Shrinkage and Selection: Wi th Application to Machine Learning: With Application to Machine Learning

by A. K. Md. Ehsanes Saleh, Mohammad Arashi, and Mina Norouzirad

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Book cover for Rank-Based Methods for Shrinkage and Selection: Wi th Application to Machine Learning

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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
  • ISBN10 1119625394
  • ISBN13 9781119625391
  • Publish Date 11 March 2022 (first published 4 March 2022)
  • Publish Status Active
  • Publish Country US
  • Publisher John Wiley and Sons Ltd
  • Imprint Wiley-Blackwell
  • Format Hardcover
  • Pages 480
  • Language English