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

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A. K. Md. Ehsanes Saleh, PhD, is a Professor Emeritus and Distinguished Professor in the School of Mathematics and Statistics, Carleton University, Ottawa, Canada. He is Fellow of IMS, ASA and Honorary member of SSC, Canada.

Mohammad Arashi, PhD, is an Associate Professor at Ferdowsi University of Mashhad in Iran and Extraordinary Professor and C2 rated researcher at University of Pretoria, Pretoria, South Africa. He is an elected member of ISI.

Resve A. Saleh, M.Sc, PhD (Berkeley), is a Professor Emeritus in the Department of ECE at the University of British Columbia, Vancouver, Canada, and formerly with University of Illinois and Stanford University. He is the author of 4 books and Fellow of the IEEE.

Mina Norouzirad, PhD, is a post-doctoral researcher at the Center for Mathematics and Applications (CMA) of Nova University of Lisbon, Portugal.

De la contraportada

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