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Activity Number: 198 - SPEED: Nonparametric Statistics: Estimation, Testing, and Modeling
Type: Contributed
Date/Time: Monday, July 30, 2018 : 11:35 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #332535
Title: Wasserstein Gradients for the Temporal Evolution of Probability Distributions
Author(s): Yaqing Chen* and Hans Mueller
Companies: University of California, Davis and UC Davis
Keywords: Estimation; Derivatives; Density Functions; Income Distributions; Evolution of Mortality; Height Growth

While many studies have been conducted on flows of probability measures, often in terms of gradient flows, modeling of the instantaneous evolution of observed distribution flows over time has not yet been explored. Our goal is to develop statistical models to reflect the observed flow of distributions in one-dimensional Euclidean space over time, based on the Wasserstein distance and corresponding optimal transport maps. For this purpose, we introduce Wasserstein temporal gradients, the notion of derivatives of optimal transport maps with respect to time. An implementation for empirical data is presented and it has been shown that it provides a consistent estimator of the derivative. These time dynamics of optimal transport maps are illustrated with time-varying distribution data that include yearly income distributions, the evolution of mortality over calendar years, and data on age-dependent height distributions of children from the longitudinal Z├╝rich growth study.

Authors who are presenting talks have a * after their name.

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