B0368
Title: Goodness-of-fit test for noisy directional data
Authors: Peter T Kim - University of Guelph (Canada)
Ja Yong Koo - Korea University (Korea, South)
Claire Lacour - University Paris Sud Orsay (France)
Thanh Mai Pham Ngoc - University Paris Sud Orsay (France) [presenting]
Abstract: The aim is to consider the nonparametric goodness-of-fit test of the uniform density on the sphere when we have observations whose density is the convolution of an error density and the true underlying density. We will deal specifically with the smooth and supersmooth error case, this latter includes the Gaussian distribution. Similar to deconvolution density estimation, the smoother the error density the harder is the rate recovery of the test problem. When considering nonparametric alternatives expressed over Sobolev and analytic classes, we show that it is possible to obtain original separation rates. Furthermore, we show that our adaptive statistical procedure attains these optimal rates. Simulations and some applications in astrophysics are tackled.
