CSIS Workshop (Cosponsored by Applied Statistics Workshop, Faculty of Economics)

Date: December 18, Thursday

Time: 10:30~12:00

Place: 'Keizaigakubu-Shinto,' 12F, 'Dai-Ichi-Kyoudou-Kenkyushitsu.'

Presenter: Noel Cressie, The Ohio State University

Abstract: Nonparametric hypothesis testing for a spatial signal can involve a large number of hypotheses. For instance, two satellite images of the same scene, taken before and after an event, could be used to test a hypothesis that the event has no environmental impact. This is equivalent to testing that the mean difference of "after - before" is
zero at each of the (typically thousands of) pixels that make up the scene. In such a situation, conventional testing procedures that control the overall Type I error deteriorate as the number of hypotheses increase.
In this article, we propose a procedure called Enhanced FDR (EFDR), which is based on controlling the false discovery rate (FDR) and a concept known as generalized degrees of freedom (GDF). EFDR differs from the standard FDR procedure through its reducing of the number of hypotheses tested. This is done in two ways: first, the model is represented more parisimoniously in the wavelet domain, and second, an optimal selection of hypotheses is made using a criterion based on generalized degrees of freedom.
This research is joint with Xiatong Shen (Ohio State) and Hsin-Cheng Huang
(Academia Sinica).