Rank Based Methods Shrinkage Selection by Saleh Ehsanes (18 results)
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Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning
Saleh, A. K. Md. Ehsanes; Arashi, Mohammad; Saleh, Resve A.; Norouzirad, Mina
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Hardcover. Condition: new. 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 Select…ion: 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 selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Rank-Based Methods for Shrinkage and Selection
A. K. Md. Ehsanes Saleh, Resve A. Saleh, Mina Norouzirad, Mohammad Arashi
- Hardcover
Seller: Rarewaves.com USA, London, LONDO, United KingdomRarewaves.com USA
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Hardback. Condition: New. 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 Ap…plication 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 selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning.
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Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation 1st Edition
Saleh, A. K. Md. Ehsanes; Arashi, Mohammad; Saleh, Resve A.; Norouzirad, Mina
- Hardcover
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- First Edition
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Condition: New. 2022. 1st Edition. Hardcover. . . . . .
Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation 1st Edition
Saleh, A. K. Md. Ehsanes; Arashi, Mohammad; Saleh, Resve A.; Norouzirad, Mina
- Hardcover
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Condition: New. 2022. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.
Rank-Based Methods for Shrinkage and Selection
A. K. Md. Ehsanes Saleh, Resve A. Saleh, Mina Norouzirad, Mohammad Arashi
- Hardcover
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Hardback. Condition: New. 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 Ap…plication 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 selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning.
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Hardcover. Condition: new. 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 Select…ion: 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 selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
- Hardcover
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Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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Hardcover. Condition: new. 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 Select…ion: 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 selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
