Binary Outcome Copula Regression Model with Sampling Gradient Fitting

TL;DR

This paper introduces a novel copula regression model for binary outcomes, along with a sampling gradient fitting algorithm, supported by simulation and real data studies to demonstrate its effectiveness.

Contribution

The paper develops a new copula regression model specifically for binary responses and proposes a score gradient estimation algorithm for fitting the model.

Findings

The binary outcome copula regression model performs well in simulations.

The proposed fitting algorithm is effective on real datasets.

Copula models can extend to binary response analysis.

Abstract

Use copula to model dependency of variable extends multivariate gaussian assumption. In this paper we first empirically studied copula regression model with continous response. Both simulation study and real data study are given. Secondly we give a novel copula regression model with binary outcome, and we propose a score gradient estimation algorithms to fit the model. Both simulation study and real data study are given for our model and fitting algorithm.