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

Robust statistics is an important field in contemporary mathematics and applied statistical methods.Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learningdescribes 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 Selectionelaborates 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 selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning