Rank-Based Methods for Shrinkage and Selection (ePub)
With Application to Machine Learning
(Sprache: Englisch)
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....
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....
sofort als Download lieferbar
eBook (ePub)
109.99 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Rank-Based Methods for Shrinkage and Selection (ePub)“
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
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
Autoren-Porträt von A. K. Md. Ehsanes Saleh, Mohammad Arashi, Resve A. Saleh, Mina Norouzirad
A. K. Md. Ehsanes Saleh, PhD, is a Professor Emeritus and Distinguished Professor in the School of Mathematics and Statistics, Carleton University, Ottawa, Canada. He is Fellow of IMS, ASA and Honorary member of SSC, Canada.Mohammad Arashi, PhD, is an Associate Professor at Ferdowsi University of Mashhad in Iran and Extraordinary Professor and C2 rated researcher at University of Pretoria, Pretoria, South Africa. He is an elected member of ISI.
Resve A. Saleh, M.Sc, PhD (Berkeley), is a Professor Emeritus in the Department of ECE at the University of British Columbia, Vancouver, Canada, and formerly with University of Illinois and Stanford University. He is the author of 4 books and Fellow of the IEEE.
Mina Norouzirad, PhD, is a post-doctoral researcher at the Center for Mathematics and Applications (CMA) of Nova University of Lisbon, Portugal.
Bibliographische Angaben
- Autoren: A. K. Md. Ehsanes Saleh , Mohammad Arashi , Resve A. Saleh , Mina Norouzirad
- 2022, 1. Auflage, 480 Seiten, Englisch
- Verlag: John Wiley & Sons
- ISBN-10: 1119625424
- ISBN-13: 9781119625421
- Erscheinungsdatum: 12.04.2022
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: ePub
- Größe: 28 MB
- Mit Kopierschutz
Sprache:
Englisch
Kopierschutz
Dieses eBook können Sie uneingeschränkt auf allen Geräten der tolino Familie lesen. Zum Lesen auf sonstigen eReadern und am PC benötigen Sie eine Adobe ID.
Kommentar zu "Rank-Based Methods for Shrinkage and Selection"
0 Gebrauchte Artikel zu „Rank-Based Methods for Shrinkage and Selection“
| Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
|---|
Schreiben Sie einen Kommentar zu "Rank-Based Methods for Shrinkage and Selection".
Kommentar verfassen