seminars:stat:160310
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| seminars:stat:160310 [2016/03/03 20:40] – shang | seminars:stat:160310 [2016/05/02 01:47] (current) – aleksey | ||
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| + | 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. | ||
| + | </ | ||