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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning

Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning

Saleh, A. K. MD Ehsanes
Arashi, Mohammad
Saleh, Resve a.

Hardcover

Probability & Statistics

ISBN10: 1119625394
ISBN13: 9781119625391
Publisher: Wiley
Published: Apr 12 2022
Pages: 480
Weight: 1.78
Height: 1.06 Width: 6.00 Depth: 9.00
Language: English
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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