# Estimating Gaussian Copulas with Missing Data

arXiv: https://arxiv.org/abs/2201.05565 · HTML: https://mlsanity.com/paper/2201.05565/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 58 references that are in the map.
- First posted on arXiv 14 Jan 2022.

## Builds on (most cited first, 2 of 2)

- [Causal Inference: A Missing Data Perspective](https://mlsanity.com/paper/1712.06170/) (2017)
- [On Inverse Probability Weighting for Nonmonotone Missing at Random Data](https://mlsanity.com/paper/1411.5310/) (2014)

## Built on by (most cited first, 1 of 1)

- [Learning Causal Graphs in Manufacturing Domains using Structural Equation Models](https://mlsanity.com/paper/2210.14573/) (2022)

## Abstract (arXiv)

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.