The Energy of Data and Distance Correlation 1st Edition

Grab a fresh mathematical lens on dependence with The Energy of Data and Distance Correlation, 1st Edition by Gabor J. Szekely and Maria L. Rizzo. This authoritative text introduces intuitive, powerful tools for measuring statistical dependence that are transforming research and applied work in statistics, machine learning, econometrics, and data science worldwide.

Dive into clear explanations of energy statistics and distance correlation, accompanied by rigorous proofs and practical examples that bridge theory and application. The book demystifies why classical correlation can miss complex relationships and demonstrates how distance-based measures detect dependence between multivariate, non-linear, and high-dimensional datasets. Chapters guide you through estimation, hypothesis testing, asymptotic results, and real-world problem framing—making advanced concepts accessible to researchers, practitioners, and advanced students.

Whether you’re a statistician refining independence tests, a data scientist building robust models, or an applied researcher across North America, Europe, Asia, or beyond, this edition equips you with reproducible, scalable approaches for modern data challenges. Emphasizing clarity without sacrificing mathematical depth, it’s ideal for coursework, technical libraries, or professional reference.

Add The Energy of Data and Distance Correlation to your collection and elevate how you detect and quantify dependence in complex datasets. Order now to bring cutting-edge statistical methodology to your analyses and stay at the forefront of multivariate inference and data-driven discovery.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.