A bias-adjusted estimator in quantile regression for clustered data

02/23/2022
by   Maria Laura Battagliola, et al.
0

The manuscript discusses how to incorporate random effects for quantile regression models for clustered data with focus on settings with many but small clusters. The paper has three contributions: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed by a pseudo likelihood approach (the LQMM method) and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/31/2021

Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations

This paper examines regression-adjusted estimation and inference of unco...
research
06/11/2021

Bootstrapping Clustered Data in R using lmeresampler

Linear mixed-effects models are commonly used to analyze clustered data ...
research
02/22/2018

The use of sampling weights in the M-quantile random-effects regression: an application to PISA mathematics scores

M-quantile random-effects regression represents an interesting approach ...
research
04/05/2022

Semiparametric Approach to Estimation of Marginal and Quantile Effects

We consider a semiparametric generalized linear model and study estimati...
research
09/29/2022

Fast Inference for Quantile Regression with Tens of Millions of Observations

While applications of big data analytics have brought many new opportuni...
research
10/17/2022

Weighted Clustered Coefficients Regression Models in Survey Sampling

Regression models are studied in survey data and are widely used to cons...
research
07/29/2017

Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference

The purpose of this paper is to construct confidence intervals for the r...

Please sign up or login with your details

Forgot password? Click here to reset