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Rank-Based Methods for Shrinkage and Selection
With Application to Machine Learning
A K MD Ehsanes Saleh, Mohammad Arashi, Resve a Saleh, Mina Norouzirad
Livre relié | Anglais
189,95 €
+ 379 points
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
Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 480
- Langue:
- Anglais
Caractéristiques
- EAN:
- 9781119625391
- Date de parution :
- 12-04-22
- Format:
- Livre relié
- Format numérique:
- Genaaid
- Dimensions :
- 152 mm x 229 mm
- Poids :
- 807 g
