Smoothed empirical likelihood inference via the modified Cholesky decomposition for quantile varying coefficient models with longitudinal data
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Jing Lv [1] ; Chaohui Guo [2] ; Jibo Wu [3]
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[1] Southwest University
Southwest University
China
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[2] Chongqing Normal University
Chongqing Normal University
China
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[3] Chongqing University of Arts and Sciences
Chongqing University of Arts and Sciences
China
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- Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 28, Nº. 3, 2019, págs. 999-1032
- Idioma: inglés
- DOI: 10.1007/s11749-018-0616-0
- Texto completo no disponible (Saber más ...)
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Resumen
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.
