Rank-Based Methods for Shrinkage and Selection

<b>Rank-Based Methods for Shrinkage and Selection</b> <p><b>A practical and hands-on guide to the theory and methodology of statistical estimation based on rank</b> <p>Robust statistics is an important field in contemporary mathematics and applied statistical methods. <i>Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning</i> 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. <p><i>Rank-Based Methods for Shrinkage and Selection</i> elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: <ul><li>Development of rank theory and application of shrinkage and selection</li> <li>Methodology for robust data science using penalized rank estimators</li> <li>Theory and methods of penalized rank dispersion for ridge, LASSO and Enet</li> <li>Topics include Liu regression, high-dimension, and AR(p)</li> <li>Novel rank-based logistic regression and neural networks</li> <li>Problem sets include R code to demonstrate its use in machine learning</li></ul>