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portada Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation
Type
Physical Book
Author
Publisher
Year
2021
Language
English
Format
Hardcover
Dimensions
22.9x15.2x2.7 cm
Weight
0.81 kg.
ISBN13
9781119625391
Categories

Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation

Mohammad Arashi (Author) · Resve A. Saleh (Author) · A. K. MD Ehsanes Saleh (Author) · Wiley · Hardcover

Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation - Saleh, A. K. MD Ehsanes ; Arashi, Mohammad ; Saleh, Resve a.

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Synopsis "Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation "

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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