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SUMMARY:Testing lack-of-fit in inverse regression models - Gerda Claeskens
  (K.U.Leuven)
DTSTART:20080530T130000Z
DTEND:20080530T140000Z
UID:TALK11790@talks.cam.ac.uk
CONTACT:8047
DESCRIPTION:We propose two test statistics for use in inverse regression p
 roblems where only noisy\, indirect observations for the mean function are
  available. Both test statistics have a counterpart in classical hypothesi
 s testing\, where they are called the order selection test and the data-dr
 iven Neyman smooth test. We also introduce two model selection criteria wh
 ich extend the classical AIC and BIC to inverse regression problems. In a 
 simulation study we show that the inverse order selection and Neyman smoot
 h tests outperform their direct counterparts in many cases. The methods ar
 e applied to data arising in confocal fluorescence microscopy. Here\, imag
 es are observed with blurring (modeled as deconvolution) and stochastic er
 ror at subsequent times. The aim is then to reduce the signal to noise rat
 io by averaging over the distinct images. In this context it is relevant t
 o test whether the images are still equal (or have changed by outside infl
 uences such as moving of the object table). This is joint work with N. Bis
 santz\, H. Holzmann and A. Munk. \n
LOCATION:MR12\, CMS\, Wilberforce Road\, Cambridge\, CB3 0WB
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