# Copula-Based Density Estimation Models for Multivariate Zero-Inflated Continuous Data

arXiv: https://arxiv.org/abs/2304.00537 · HTML: https://mlsanity.com/paper/2304.00537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 2 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Integrals over Gaussians under Linear Domain Constraints](https://mlsanity.com/paper/1910.09328/) (2019)
- [Learning Distributions Generated by One-Layer ReLU Networks](https://mlsanity.com/paper/1909.01812/) (2019)
- [Some copula inference procedures adapted to the presence of ties](https://mlsanity.com/paper/1609.05519/) (2016)
- [Copula Modeling for Data with Ties](https://mlsanity.com/paper/1612.06968/) (2016)

## Abstract (arXiv)

Zero-inflated continuous data ubiquitously appear in many fields, in which lots of exactly zero-valued data are observed while others distribute continuously. Due to the mixed structure of discreteness and continuity in its distribution, statistical analysis is challenging especially for multivariate case. In this paper, we propose two copula-based density estimation models that can cope with multivariate correlation among zero-inflated continuous variables. In order to overcome the difficulty in the use of copulas due to the tied-data problem in zero-inflated data, we propose a new type of copula, rectified Gaussian copula, and present efficient methods for parameter estimation and likelihood computation. Numerical experiments demonstrates the superiority of our proposals compared to conventional density estimation methods.