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Bayesian Quantile Regression Models for Complex Survey Data Under Informative Sampling

2024/04/09 by Marcus L. Nascimento, Kelly C. M. Gonçalves · 1 voice
Mathematics · Computer Science · #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Bayesian Methods and Mixture Models

paper · doi:10.1093/jssam/smae015

openalex publication_date 2024/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22

Abstract

Abstract The interest in considering the relation among random variables in quantiles instead of the mean has emerged in various fields, and data collected from complex survey designs are of fundamental importance to different areas. Despite the extensive literature on survey data analysis and quantile regression models, research papers exploring quantile regression estimation accounting for an informative design have primarily been restricted to a frequentist framework. In this paper, we introduce different Bayesian methods relying on the survey-weighted estimator and the estimating equations. A model-based simulation study evaluates the proposed methods compared to alternative approaches and a naïve model fitting ignoring the informative sampling design under different scenarios. In addition, we illustrate and conduct a prior sensitivity analysis in a design-based simulation study that uses data from Prova Brasil 2011.

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