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DTSTAMP:20251110T154309Z
DESCRIPTION:Statistical Inference for Functional Linear Quantile Regression
 \n\n\n	Abstract:\n\n\nWe propose inferential tools for functional linear qu
 antile regression where the conditional quantile of a scalar response is a
 ssumed to be a linear functional of a functional covariate. In contrast to
  conventional approaches\, we employ kernel convolution to smooth the orig
 inal loss function. The coefficient function is estimated under a reproduc
 ing kernel Hilbert space framework. A gradient descent algorithm is design
 ed to minimize the smoothed loss function with a roughness penalty. With t
 he aid of the Banach fixed-point theorem\, we show the existence and uniqu
 eness of our proposed estimator as the minimizer of the regularized loss f
 unction in an appropriate Hilbert space. Furthermore\, we establish the co
 nvergence rate as well as the weak convergence of our estimator. As far as
  we know\, this is the first weak convergence result for a functional quan
 tile regression model. Pointwise confidence intervals and a simultaneous c
 onfidence band for the true coefficient function are then developed based 
 on these theoretical properties. Numerical studies including both simulati
 ons and a data application are conducted to investigate the performance of
  our estimator and inference tools in finite sample.\n\n\n	Speaker\n\n\nPei
 jun Sang is an Assistant Professor in the Department of Statistics and Act
 uarial Science at the University of Waterloo. His research interest is foc
 used on functional data analysis\, high dimensional statistics\, dependenc
 e modelling with copula models for discrete and/or survival outcomes.\n\nM
 cGill Statistics Seminar schedule: https://mcgillstat.github.io/\n\n\n	McGi
 ll Statistics Seminar\n\n		Zoom Link\n\n		\n			https://mcgill.zoom.us/j/834366862
 93?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09\n\n			Meeting ID: 834 3668 6293\n\n			Pa
 sscode: 12345\n		\n	\n\n	\n		 \n	\n\n
DTSTART:20220916T191500Z
DTEND:20220916T201500Z
SUMMARY:Peijun Sang (University of Waterloo)
URL:/mathstat/channels/event/peijun-sang-university-wa
 terloo-341966
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