Estimation for bivariate quantile varying coefficient model-Linglong Kong (加拿大University of Alberta数学与统计学系)

We propose a bivariate quantile regression method for the bivariate varying

coefficient model through a directional approach. The varying coefficients

are approximated by the B-spline basis and an L2-type penalty is imposed

to achieve desired smoothness. We develop a multistage estimation procedure

 based on the Propagation-Separation (PS) approach to borrow information

from nearby directions. The PS method is capable of handling the

computational complexity raised by simultaneously considering multiple

directions to efficiently estimate varying coefficients while guaranteeing

 certain smoothness along directions. We reformulate the optimization

problem and solve it by the Alternating Direction Method of Multipliers

(ADMM), which is implemented using R while the core is written in C to

 speed it up. Simulation studies are conducted to confirm the finite sample

 performance of our proposed method. A real data on Diffusion Tensor

Imaging (DTI) properties from a clinical study on neurodevelopment is

analyzed. Joint work with Haoxu Shu, Qianchuan Chad He, Giseon Heo, John

 Gilmore, and Hongtu Zhu.