Name: Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation (en Inglés)
Price: 145.13 USD
Availability: InStock
Author: Saleh, A. K. MD Ehsanes ; Arashi, Mohammad ; Saleh, Resve a.
ISBN: 9781119625391

portada Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation (en Inglés)

Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation (en Inglés)

Formato

Libro Físico

Autor

Mohammad Arashi

Resve A. Saleh

A. K. MD Ehsanes Saleh

Editorial

Wiley

Año

2021

Idioma

Inglés

Encuadernación

Tapa Dura

Dimensiones

22.9 x 15.2 x 2.7 cm

Peso

0.81 kg.

ISBN13

9781119625391

Categorías

Probabilidad y estadística

Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation (en Inglés)

Mohammad Arashi (Autor) · Resve A. Saleh (Autor) · A. K. MD Ehsanes Saleh (Autor) · Wiley · Tapa Dura

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Reseña del libro "Rank-Based Statistical Theory and Methods for Selection and Shrinkage Estimation (en Inglés)"

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