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