Rank-Based Methods for Shrinkage and Selection

With Application to Machine Learning

A K Md Ehsanes Saleh author Mohammad Arashi author Resve A Saleh author Mina Norouzirad author

Format:Hardback

Publisher:John Wiley & Sons Inc

Published:11th Mar '22

Currently unavailable, and unfortunately no date known when it will be back

Rank-Based Methods for Shrinkage and Selection cover

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

ISBN: 9781119625391

Dimensions: 10mm x 10mm x 10mm

Weight: 454g

480 pages