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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 in Franklin, TN
Current price: $139.95
Barnes and Noble
Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning in Franklin, TN
Current price: $139.95
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Size: Hardcover
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.
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.
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
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.
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.
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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