Binary Outcome Copula Regression Model with Sampling Gradient Fitting

01/03/2021
by   Weijian Luo, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/02/2021

Zero-inflated generalized extreme value regression model for binary data and application in health study

Logistic regression model is widely used in many studies to investigate ...
research
03/03/2023

Estimation of logistic regression parameters for complex survey data: a real data based simulation study

In complex survey data, each sampled observation has assigned a sampling...
research
08/06/2019

Policy Evaluation with Latent Confounders via Optimal Balance

Evaluating novel contextual bandit policies using logged data is crucial...
research
03/19/2022

When regression coefficients change over time: A proposal

A common approach in forecasting problems is to estimate a least-squares...
research
05/16/2022

CurFi: An automated tool to find the best regression analysis model using curve fitting

Regression analysis is a well known quantitative research method that pr...
research
03/08/2015

Fitting 3D Morphable Models using Local Features

In this paper, we propose a novel fitting method that uses local image f...
research
07/29/2021

The Bradly-Terry Regression Trunk approach for modelling preference data with small trees

This paper introduces the Bradley-Terry Regression Trunk model, a novel ...

Please sign up or login with your details

Forgot password? Click here to reset