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Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning.
・ISBN 978-1-119-62539-1 hard US$ 143.95
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電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-1-119-62543-8
| 著者・編者 | Saleh, A. K. Md. Ehsanes / Arashi, Mohammad et al., |
|---|---|
| 出版社 | (Wiley, US) |
| 出版年月 | 2022.04 |
| ページ数 | 480 pp. |
| 言語 | ENG |
| ニュース番号 | ＜671-271＞ |
解説
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