Rank-Based Methods for Shrinkage and Selection
With Application to Machine Learning
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Rank-Based Methods for Shrinkage and Selection
With Application to Machine Learning
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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Table of contents
- Rank-Based Methods for Shrinkage and Selection
- Contents in Brief
- Contents
- List of Figures
- List of Tables
- Foreword
- Preface
- 1 Introduction to Rank-based Regression
- 2 Characteristics of Rank-based Penalty Estimators
- 3 Location and Simple Linear Models
- 4 Analysis of Variance (ANOVA)
- 5 Seemingly Unrelated Simple Linear Models
- 6 Multiple Linear Regression Models
- 7 Partially Linear Multiple Regression Model
- 8 Liu Regression Models
- 9 Autoregressive Models
- 10 High-Dimensional Models
- 11 Rank-based Logistic Regression
- 12 Rank-based Neural Networks
- Bibliography
- Author Index
- Subject Index
- EULA