Energy statistics: a class of statistics based on distances. 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. Thus even if the observations are complex objects, like functions, one can use their real valued nonnegative distances for inference. Theory and application of energy statistics are discussed and illustrated. Finally, we explore the notion of potential and kinetic energy of goodness-of-fit.

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  1. Pronzato, Luc; Zhigljavsky, Anatoly: Minimum-energy measures for singular kernels (2021)
  2. Herwartz, Helmut; Maxand, Simone: Nonparametric tests for independence: a review and comparative simulation study with an application to malnutrition data in India (2020)
  3. Hlávka, Zdeněk; Hušková, Marie; Meintanis, Simos G.: Change-point methods for multivariate time-series: paired vectorial observations (2020)
  4. Li, Gongkai; Tang, Minh; Charon, Nicolas; Priebe, Carey: Central limit theorems for classical multidimensional scaling (2020)
  5. Lovato, Ilenia; Pini, Alessia; Stamm, Aymeric; Vantini, Simone: Model-free two-sample test for network-valued data (2020)
  6. Pronzato, Luc; Zhigljavsky, Anatoly: Bayesian quadrature, energy minimization, and space-filling design (2020)
  7. Sang, Yongli; Dang, Xin: Empirical likelihood test for diagonal symmetry (2020)
  8. Sarkar, Soham; Biswas, Rahul; Ghosh, Anil K.: On some graph-based two-sample tests for high dimension, low sample size data (2020)
  9. Shen, Cencheng; Priebe, Carey E.; Vogelstein, Joshua T.: From distance correlation to multiscale graph correlation (2020)
  10. Chakraborty, Shubhadeep; Zhang, Xianyang: Distance metrics for measuring joint dependence with application to causal inference (2019)
  11. Chen, Feifei; Meintanis, Simos G.; Zhu, Lixing: On some characterizations and multidimensional criteria for testing homogeneity, symmetry and independence (2019)
  12. Cui, Hengjian; Zhong, Wei: A distribution-free test of independence based on mean variance index (2019)
  13. Curry, Jamye; Dang, Xin; Sang, Hailin: A rank-based Cramér-von-Mises-type test for two samples (2019)
  14. Dette, Holger; Pepelyshev, Andrey; Zhigljavsky, Anatoly: The BLUE in continuous-time regression models with correlated errors (2019)
  15. Febrero-Bande, Manuel; González-Manteiga, Wenceslao; Oviedo de la Fuente, Manuel: Variable selection in functional additive regression models (2019)
  16. Jiang, Qing; Hušková, Marie; Meintanis, Simos G.; Zhu, Lixing: Asymptotics, finite-sample comparisons and applications for two-sample tests with functional data (2019)
  17. Lee, Sangyeol; Meintanis, Simos G.; Jo, Minyoung: Inferential procedures based on the integrated empirical characteristic function (2019)
  18. Nguyen, Hien D.: An introduction to approximate Bayesian computation (2019)
  19. Pronzato, Luc; Wynn, Henry P.; Zhigljavsky, Anatoly: Bregman divergences based on optimal design criteria and simplicial measures of dispersion (2019)
  20. Talebi, Hassan; Mueller, Ute; Tolosana-Delgado, Raimon; van den Boogaart, K. Gerald: Geostatistical simulation of geochemical compositions in the presence of multiple geological units: application to mineral resource evaluation (2019)

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