Rank-Based Methods for Shrinkage and Selection: Wi th Application to Machine Learning, Hardcover Book, By: AK Saleh

Rank-Based Methods for Shrinkage and Selection 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

• Books Author: AK Saleh
• Number Of Pages: 480
• Language: English
• Publisher: John Wiley and Sons Ltd
• Books Category: Education & Teaching
• Book Format: Hardcover
• Books_ISBN: 9781119625391
• Other Feature 2: Size: 152 x 239 x 34 mm
• Other Feature 3: Weight: 804g
• Country Of Origin: United States of America
• Publication Date: April 12, 2022