Estimating Gaussian Copulas with Missing Data
Machine Learning
2022-01-17 v1 Machine Learning
Methodology
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.
Cite
@article{arxiv.2201.05565,
title = {Estimating Gaussian Copulas with Missing Data},
author = {Maximilian Kertel and Markus Pauly},
journal= {arXiv preprint arXiv:2201.05565},
year = {2022}
}