Estimation and Applications of Quantile Regression for Binary Longitudinal Data

09/12/2019
by   Mohammad Arshad Rahman, et al.
Indian Institute of Technology Kanpur
0

This paper develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

READ FULL TEXT
10/23/2020

A two-part finite mixture quantile regression model for semi-continuous longitudinal data

This paper develops a two-part finite mixture quantile regression model ...
02/17/2020

Bayesian Quantile Factor Models

Factor analysis is a flexible technique for assessment of multivariate d...
10/18/2019

Noncrossing structured additive multiple-output Bayesian quantile regression models

Quantile regression models are a powerful tool for studying different po...
09/28/2020

Quantile Regression Neural Networks: A Bayesian Approach

This article introduces a Bayesian neural network estimation method for ...
10/04/2018

Regression Analyses of Distributions using Quantile Functional Regression

Radiomics involves the study of tumor images to identify quantitative ma...
04/30/2023

Quantile regression for longitudinal functional data with application to feed intake of lactating sows

This article focuses on the study of lactating sows, where the main inte...

Please sign up or login with your details

Forgot password? Click here to reset