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SUMMARY:Goodness-of-fit tests for noisy directional data - Thanh Mai Pham 
 Ngoc\, Université Paris Sud\, Orsay
DTSTART:20120217T143000Z
DTEND:20120217T153000Z
UID:TALK35200@talks.cam.ac.uk
CONTACT:Richard Samworth
DESCRIPTION:The spherical convolution model provides a setup where each ge
 nuine\n observation Xi belonging to S2 the unit sphere of R3\, is contamin
 ated\n by a small random rotation. The aim of the present work is to\nprov
 ide nonparametric adaptive minimax goodness-of-fit testing\nprocedures on 
 f\, the density of Xi from noisy\nobservations. More precisely\, let f0 be
 ing the uniform density on S2\,\nwe consider the problem\n of testing the 
 the null hypothesis f = f0 with alternatives expressed\n in L2 norm over S
 obolev\nclass.
LOCATION:MR12\, CMS\, Wilberforce Road\, Cambridge\, CB3 0WB
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