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Statistics Seminar
Department of Mathematical Sciences
DATE: | Thursday, March 10, 2016 |
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TIME: | 1:15pm to 2:15pm |
LOCATION: | WH 100E |
SPEAKER: | Zuofeng Shang, Binghamton University |
TITLE: | Nonparametric Inference In Functional Data |
Abstract
We propose a roughness regularization approach in making nonparametric inference for generalized functional linear models. In a reproducing kernel Hilbert space framework, we construct asymptotically valid confidence intervals for regression mean, prediction intervals for future response and various statistical procedures for hypothesis testing. In particular, one procedure for testing global behaviors of the slope function is adaptive to the smoothness of the slope function and to the structure of the predictors.