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

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Yes, you can access Rank-Based Methods for Shrinkage and Selection by A. K. Md. Ehsanes Saleh,Mohammad Arashi,Resve A. Saleh,Mina Norouzirad in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.