Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10151175 | Computational Statistics & Data Analysis | 2019 | 19 Pages |
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
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Xia Cui, Weihua Zhao, Heng Lian, Hua Liang,