主  題：Estimation for bivariate quantile varying coefficient model

內容簡介：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.

報告人：Linglong Kong   博導

時  間：2016-12-23    09:00

地  點：競慧東樓305

舉辦單位：理學院 統計學與大數據研究院 科研部