Mohammad Arashi

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 selectionMethodology for robust data science using penalized rank estimatorsTheory and methods of penalized rank dispersion for ridge, LASSO and EnetTopics include Liu regression, high-dimension, and AR(p)Novel rank-based logistic regression and neural networksProblem sets include R code to demonstrate its use in machine learning

A guide to the systematic analytical results for ridge, LASSO, preliminary test, and Stein-type estimators with applications Theory of Ridge Regression Estimation with Applications offers a comprehensive guide to the theory and methods of estimation. Ridge regression and LASSO are at the center of all penalty estimators in a range of standard models that are used in many applied statistical analyses. Written by noted experts in the field, the book contains a thorough introduction to penalty and shrinkage estimation and explores the role that ridge, LASSO, and logistic regression play in the computer intensive area of neural network and big data analysis. Designed to be accessible, the book presents detailed coverage of the basic terminology related to various models such as the location and simple linear models, normal and rank theory-based ridge, LASSO, preliminary test and Stein-type estimators.? The authors also include problem sets to enhance learning. This book is a volume in the Wiley Series in Probability and Statistics series that provides essential and invaluable reading for all statisticians. This important resource: Offers theoretical coverage and computer-intensive applications of the procedures presentedContains solutions and alternate methods for prediction accuracy and selecting model proceduresPresents the first book to focus on ridge regression and unifies past research with current methodologyUses R throughout the text and includes a companion website containing convenient data sets Written for graduate students, practitioners, and researchers in various fields of science, Theory of Ridge Regression Estimation with Applications is an authoritative guide to the theory and methodology of statistical estimation.

This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors: Includes a wide array of applications for the analysis of multivariate observationsEmphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematicsContains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topicAddresses linear regression models with non-normal errors with practical real-world examplesUniquely addresses regression models in Student's t-distributed errors and t-modelsSupplemented with an Instructor's Solutions Manual, which is available via written request by the Publisher