Estimating Gaussian Copulas with Missing Data

• URL: http://arxiv.org/abs/2201.05565v1
• Date: Fri, 14 Jan 2022 17:20:44 GMT
• Title: Estimating Gaussian Copulas with Missing Data
• Authors: Maximilian Kertel and Markus Pauly
• Abstract summary: We show how to circumvent a priori assumptions on the marginals with semiparametric modelling. The joint distribution learned through this algorithm is considerably closer to the underlying distribution than existing methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data. We further show how to circumvent a priori assumptions on the marginals with semiparametric modelling. The joint distribution learned through this algorithm is considerably closer to the underlying distribution than existing methods.

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