Rank-Based Methods for Shrinkage and Selection
Mohammad Arashi, Mina Norouzirad, A. K. Md. Ehsanes Saleh, et al.
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Naturwissenschaften, Medizin, Informatik, Technik / Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
Beschreibung
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
Kundenbewertungen
Regressionsanalyse, Ökonometrie u. statistische Methoden, Applied Probability & Statistics, Econometric & Statistical Methods, Regression Analysis, Ökonometrie, Angewandte Wahrscheinlichkeitsrechnung u. Statistik, Statistics, Statistik