Rank-Based Methods for Shrinkage and Selection
eBook - PDF

Rank-Based Methods for Shrinkage and Selection

With Application to Machine Learning

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Rank-Based Methods for Shrinkage and Selection

With Application to Machine Learning

Book details
Table of contents
Citations

About This Book

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

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Yes, you can access Rank-Based Methods for Shrinkage and Selection by A. K. Md. Ehsanes Saleh, Mohammad Arashi, Resve A. Saleh, Mina Norouzirad in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2022
ISBN
9781119625414
Edition
1

Table of contents

  1. Rank-Based Methods for Shrinkage and Selection
  2. Contents in Brief
  3. Contents
  4. List of Figures
  5. List of Tables
  6. Foreword
  7. Preface
  8. 1 Introduction to Rank-based Regression
  9. 2 Characteristics of Rank-based Penalty Estimators
  10. 3 Location and Simple Linear Models
  11. 4 Analysis of Variance (ANOVA)
  12. 5 Seemingly Unrelated Simple Linear Models
  13. 6 Multiple Linear Regression Models
  14. 7 Partially Linear Multiple Regression Model
  15. 8 Liu Regression Models
  16. 9 Autoregressive Models
  17. 10 High-Dimensional Models
  18. 11 Rank-based Logistic Regression
  19. 12 Rank-based Neural Networks
  20. Bibliography
  21. Author Index
  22. Subject Index
  23. EULA