Utvidet returrett til 31. januar 2024

Rank-Based Methods for Shrinkage and Selection

- With Application to Machine Learning

Om Rank-Based Methods for Shrinkage and Selection

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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  • Språk:
  • Engelsk
  • ISBN:
  • 9781119625391
  • Bindende:
  • Hardback
  • Sider:
  • 480
  • Utgitt:
  • 11. mars 2022
  • Dimensjoner:
  • 10x10x10 mm.
  • Vekt:
  • 454 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 29. november 2024

Beskrivelse av Rank-Based Methods for Shrinkage and Selection

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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