Negative Definite Kernels and Metrics: Recovering Measures from Potentials

Svetlozar T. Rachev, Lev B. Klebanov, Stoyan V. Stoyanov and Frank J. Fabozzi
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Svetlozar T. Rachev: Stony Brook University, Department of Applied Mathematics and Statistics College of Business
Lev B. Klebanov: Charles University, Department of Probability and Statistics
Stoyan V. Stoyanov: EDHEC Business School EDHEC-Risk Institute
Frank J. Fabozzi: EDHEC Business School EDHEC-Risk Institute

Chapter Chapter 22 in The Methods of Distances in the Theory of Probability and Statistics, 2013, pp 539-569 from Springer

Abstract: Abstract Introduce probability metrics through strongly negative definite kernel functions and provide examples, Introduce probability metrics through m-negative definite kernels and provide examples, Introduce the notion of potential corresponding to a probability measure, Present the problem of recovering a probability measure from its potential, Consider the relation between the problems of convergence of measures and the convergence of their potentials, Characterize probability distributions using the theory of recovering probability measures from potentials.

Keywords: Negative Definite Kernel; Characterizing Probability Distributions; Introductory Probability; Probability Metrics; Symmetric Positive Definite Kernel (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-4869-3_22

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DOI: 10.1007/978-1-4614-4869-3_22

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