A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

Liya Fu, You-Gan Wang and Min Zhu

Computational Statistics & Data Analysis, 2015, vol. 84, issue C, 41-53

Abstract: To enhance the efficiency of regression parameter estimation by modeling the correlation structure of correlated binary error terms in quantile regression with repeated measurements, we propose a Gaussian pseudolikelihood approach for estimating correlation parameters and selecting the most appropriate working correlation matrix simultaneously. The induced smoothing method is applied to estimate the covariance of the regression parameter estimates, which can bypass density estimation of the errors. Extensive numerical studies indicate that the proposed method performs well in selecting an accurate correlation structure and improving regression parameter estimation efficiency. The proposed method is further illustrated by analyzing a dental dataset.

Keywords: Gaussian estimation; Induced smoothing method; Pseudolikelihood; Repeated measurements; Working covariance matrix (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:84:y:2015:i:c:p:41-53

DOI: 10.1016/j.csda.2014.11.002

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