The Energy of Data and Distance Correlation

Energy distance is a statistical distance between random vector distributions, derived from Newton's gravitational potential energy. This book introduces energy statistics, which are functions of distances between observations in metric spaces, and demonstrates their application in various statistical methods using R. It is intended for teachers and students, providing a comprehensive overview of the field and its applications.

Format: Hardback
Length: 448 pages
Publication date: 14 February 2023
Publisher: Apple Academic Press Inc.

Energy distance is a statistical measure that quantifies the dissimilarity between the distributions of random vectors, emphasizing their equality. This concept finds its roots in Newton's gravitational potential energy and exhibits a captivating relationship with the notion of potential energy in the realm of statistical observations. Energy statistics revolve around the analysis of distances between statistical observations within metric spaces. The authors of this book aspire to ignite the curiosity of statisticians who have yet to delve into the realm of E-statistics, encouraging them to harness these novel methods through the powerful R programming language.

The Energy of Data and Distance Correlation is designed primarily for educators and students seeking specialized resources on energy statistics. However, it can also serve as a valuable supplement to a diverse array of courses and disciplines, including Monte Carlo methods, U-statistics, V-statistics, measures of multivariate dependence, goodness-of-fit tests, nonparametric methods, and distance-based methods.

E-statistics offers a robust set of tools for addressing challenges in multivariate inference and analysis. These methods are implemented in R, enabling readers to apply them seamlessly using the freely available energy package for R. The proposed book aims to provide a comprehensive overview of the current state-of-the-art in the development of energy statistics, as well as an insightful exploration of their diverse applications. A comprehensive background and literature review will be particularly valuable for individuals considering further research or practical applications in the field of energy statistics.

Weight: 798g
Dimension: 241 x 163 x 31 (mm)
ISBN-13: 9781482242744