Pursuit of dynamic structure in quantile additive models with longitudinal data

Xia Cui, Weihua Zhao, Heng Lian and Hua Liang

Computational Statistics & Data Analysis, 2019, vol. 130, issue C, 42-60

Abstract: We consider quantile additive models with dynamic (time-varying) component functions. We allow some of the component functions to be non-dynamic, and show, as expected but technically nontrivially, that estimators of the non-dynamic functions have a faster convergence rate. A penalization-based method, called dynamic structure pursuit, is proposed to automatically identify these non-dynamic functions. Finally, in the sparse setting, a four-stage estimation procedure is proposed which first identifies the nonzero component functions and then applies the identification strategy of the non-dynamic functions. Theoretical and numerical results are provided to illustrate the performance of the estimators.

Keywords: B-splines; Dynamic structure pursuit; Quantile regression; Sparse functional data (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:130:y:2019:i:c:p:42-60

DOI: 10.1016/j.csda.2018.08.017

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