{"product_id":"rank-based-methods-for-shrinkage-and-selection-9781119625391","title":"Rank-Based Methods for Shrinkage and Selection","description":"\u003cb\u003eRank-Based Methods for Shrinkage and Selection\u003c\/b\u003e \u003cp\u003e\u003cb\u003eA practical and hands-on guide to the theory and methodology of statistical estimation based on rank\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eRobust statistics is an important field in contemporary mathematics and applied statistical methods. \u003ci\u003eRank-Based Methods for Shrinkage and Selection: With Application to Machine Learning\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eRank-Based Methods for Shrinkage and Selection\u003c\/i\u003e elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDevelopment of rank theory and application of shrinkage and selection\u003c\/li\u003e \u003cli\u003eMethodology for robust data science using penalized rank estimators\u003c\/li\u003e \u003cli\u003eTheory and methods of penalized rank dispersion for ridge, LASSO and Enet\u003c\/li\u003e \u003cli\u003eTopics include Liu regression, high-dimension, and AR(p)\u003c\/li\u003e \u003cli\u003eNovel rank-based logistic regression and neural networks\u003c\/li\u003e \u003cli\u003eProblem sets include R code to demonstrate its use in machine learning\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e A. K. MD Ehsanes Saleh\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Wiley\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 04\/12\/2022\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 480\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.78lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.00h x 6.00w x 1.06d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781119625391\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eA. K. Md. EHSANES SALEH\u003c\/b\u003e, PhD, is a professor emeritus and distinguished research professor in the school of Mathematics and Statistics, Carleton University, Canada. Dr. Saleh is author of \u003ci\u003eTheory of Preliminary Test and Stein-Type Estimation with Applications\u003c\/i\u003e, and co-author of \u003ci\u003eAn Introduction to Probability and Statistics\u003c\/i\u003e, 2nd Edition, \u003ci\u003eStatistical Inference for Models with Multivariate t-Distributed Errors\u003c\/i\u003e, and \u003ci\u003eTheory of Ridge Regression Estimation with Applications\u003c\/i\u003e, all published by Wiley.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eM. Arashi\u003c\/b\u003e, PhD, is an Associate Professor at Shahrood University of Technology, Iran and Extraordinary Professor and C2 rated researcher at University of Pretoria, South Africa. Dr. Arashi is co-author of \u003ci\u003eStatistical Inference for Models with Multivariate t-Distributed Errors\u003c\/i\u003e and \u003ci\u003eTheory of Ridge Regression Estimation with Applications\u003c\/i\u003e, both published by Wiley.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eMina Norouzirad\u003c\/b\u003e, PhD, is Lecturer at Faculty of Mathematical Sciences, Shahrood University of Technology, Iran.\u003c\/p\u003e\u003cbr\u003e","brand":"Wiley","offers":[{"title":"Hardcover","offer_id":40690980487283,"sku":"9.78E+12","price":196.23,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/products\/img_17ce6d5e-72bf-4de2-b3d9-cac2021b3c0f.jpg?v=1675350373","url":"https:\/\/bookstorenmore.com\/products\/rank-based-methods-for-shrinkage-and-selection-9781119625391","provider":"Bookstore N More","version":"1.0","type":"link"}