【online】High-Dimensional Spatial Quantile Function-on-Scalar Regression
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题目: High-Dimensional Spatial Quantile Function-on-Scalar Regression
报告人：Dr. Bei Jiang (Department of Mathematical and Statistical Sciences, University of Alberta, Canada)
报告方式：腾讯会议平台 ID：140 324 213
摘要: With modern technology development, functional data are often observed in various scientific fields. Quantile regression has become an important statistical methodology. In this talk, we develop a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar covariates at different quantile levels and also gives a practical way to generate new images for given covariate values. Theoretically, we establish the minimax rates of convergence for estimating coefficient functions under both fixed and random designs. We further develop an efficient primal-dual algorithm to handle high-dimensional image data. Simulations and real data analysis are conducted to examine the finite-sample performance. Joint work with Zhengwu Zhang, Xiao Wang and Hongtu Zhu.
报告人简介：Dr. Bei Jiang is an Assistant Professor at the Department of Mathematical and Statistical Sciences, University of Alberta. Her statistical research interests include Bayesian latent variable/hierarchical modeling methods, joint modeling of multi-view data/data integration, and statistical disclosure control methods to balance disclosure risk and inferential integrity. She has worked closely with collaborators in women’s health, mental health, neurology, ecology, and worked with industry partners to apply cutting-edge statistical machine learning methods to real-world applications.