Székely, Gábor J. 1947- creator author. Rizzo, Maria L. author. text flu BOCA RATON CHAPMAN & HALL CRC 2023 monographic eng

electronic resource

1 online resource Energy distance is a statistical distance between the distributions of random vectors, which characterizes equality of distributions. The name energy derives from Newton's gravitational potential energy, and there is an elegant relation to the notion of potential energy between statistical observations. Energy statistics are functions of distances between statistical observations in metric spaces. The authors hope this book will spark the interest of most statisticians who so far have not explored E-statistics and would like to apply these new methods using R. The Energy of Data and Distance Correlation is intended for teachers and students looking for dedicated material on energy statistics, but can serve as a supplement to a wide range of courses and areas, such as Monte Carlo methods, U-statistics or V-statistics, measures of multivariate dependence, goodness-of-fit tests, nonparametric methods and distance based methods. E-statistics provides powerful methods to deal with problems in multivariate inference and analysis. Methods are implemented in R, and readers can immediately apply them using the freely available energy package for R. The proposed book will provide an overview of the existing state-of-the-art in development of energy statistics and an overview of applications. Background and literature review is valuable for anyone considering further research or application in energy statistics. MATHEMATICS / Probability & Statistics / General Mathematical statistics Data processing Statistics Data processing R (Computer program language) Statistical methods Force and energy QA276.4 0429157150 9780429157158 9780429543968 0429543964 9781482242751 1482242753 9780429529269 0429529260 https://www.taylorfrancis.com/books/9780429157158 http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf https://www.taylorfrancis.com/books/9780429157158 http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf OCoLC-P 221208 20251030122225.0 9780429157158 eng