Smoothed empirical likelihood inference via the modified Cholesky decomposition for quantile varying coefficient models with longitudinal data

Jing Lv, Chaohui Guo () and Jibo Wu
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Jing Lv: Southwest University
Chaohui Guo: Chongqing Normal University
Jibo Wu: Chongqing University of Arts and Sciences

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2019, vol. 28, issue 3, No 20, 999-1032

Abstract: Abstract It is essential to deal with the within-subject correlation among repeated measures over time to improve statistical inference efficiency. However, it is a challenging task to correctly specify a working correlation in quantile regression with longitudinal data. In this paper, we first develop an adaptive approach to estimate the within-subject covariance matrix of quantile regression by applying a modified Cholesky decomposition. Then, weighted kernel GEE-type quantile estimating equations are proposed for varying coefficient functions. Note that the proposed estimating equations include a discrete indicator function, which results in some problems for computation and asymptotic analysis. Thus, we construct smoothed estimating equations by introducing a bounded kernel function. Furthermore, we develop a smoothed empirical likelihood method to improve the accuracy of interval estimation. Finally, simulation studies and a real data analysis indicate that the proposed method has superior advantages over the existing methods in terms of coverage accuracies and widths of confidence intervals.

Keywords: Confidence band; Longitudinal data; Modified Cholesky decomposition; Quantile regression; Robustness and efficiency; 62G08; 62G20; 62G35 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11749-018-0616-0

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