Rank-Based Methods for Shrinkage and Selection

Language: English

Published by John Wiley and Sons Inc, US, 2022

1119625394 / 9781119625391

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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 selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning.

Seller Inventory # LU-9781119625391

Title
Rank-Based Methods for Shrinkage and Selection
Author
A. K. Md. Ehsanes Saleh, Resve A. Saleh, Mina Norouzirad, Mohammad Arashi
Publisher
John Wiley and Sons Inc, US
Publication year
2022
Condition
New
Binding
Hardback
Language
English
ISBN 10
1119625394
ISBN 13
9781119625391
Item weight
804 grams

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