Abstract #300631

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JSM 2003 Abstract #300631
Activity Number: 445
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #300631
Title: Nonparametric Hypothesis Testing for a Spatial Signal
Author(s): Hsin-Cheng Huang*+ and Xiaotong Shen and Noel A. C. Cressie
Companies: Academia Sinica and Ohio State University and Ohio State University
Address: Institute of Statistical Science, Taipei, 115, Taiwan
Keywords: denoising ; false discovery rate ; generalized degrees of freedom ; power ; signal detection ; wavelet
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. In such a situation, conventional testing procedures that control the overall Type I error deteriorate as the number of hypotheses increase. 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 parsimoniously in the wavelet domain, and second, an optimal selection of hypotheses is made using a criterion based on GDF. Moreover, if a signal is deemed present, EFDR can indicate its location and magnitude. We examine EFDR's operating characteristics, and in simulations we show that it outperforms the standard FDR and conventional testing procedures.

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