A streaming algorithm for bivariate empirical copulas

Alastair Gregory

Computational Statistics & Data Analysis, 2019, vol. 135, issue C, 56-69

Abstract: Empirical copula functions can be used to model the dependence structure of multivariate data. The Greenwald and Khanna algorithm is adapted in order to provide a space-memory efficient approximation to the empirical copula function of a bivariate stream of data. A succinct space-memory efficient summary of values seen in the stream up to a certain time is maintained and can be queried at any point to return an approximation to the empirical bivariate copula function with guaranteed error bounds. An example then illustrates how these summaries can be used as a tool to compute approximations to higher dimensional copula decompositions containing bivariate copulas. The computational benefits and approximation error of the algorithm are theoretically and numerically assessed.

Keywords: Copulas; Statistical summaries; Dependent data streams (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:135:y:2019:i:c:p:56-69

DOI: 10.1016/j.csda.2019.01.015

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