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