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

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Synopsis

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

Book details

Author:
A. K. Saleh, Mohammad Arashi, Resve A. Saleh, Mina Norouzirad
ISBN:
9781119625421
Related ISBNs:
9781119625438, 9781119625391
Publisher:
Wiley
Pages:
300
Reading age:
Not specified
Includes images:
Yes
Date of addition:
2022-04-14
Usage restrictions:
Copyright
Copyright date:
2022
Copyright by:
John Wiley and Sons, Inc. 
Adult content:
No
Language:
English
Categories:
Mathematics and Statistics, Nonfiction