<a href="http://www.stat.lsa.umich.edu/~thsing/">University of Michigan</a>
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In the study of intrinsically stationary spatial processes, a new nonparametric variogram estimator is proposed through its spectral representation.  The methodology is based on estimation of the variogram's spectrum, here for the isotropic case, which is formulated in terms of solving a regularized inverse problem. We use quadratic programming to obtain the solution. The estimated variogram is guaranteed to be conditionally negative-definite, a key property of variograms.  Simulation shows that our estimator is flexible and generally has smaller mean integrated squared error than the parametric estimator under model misspecification.

UGA/Clemson Joint Seminar

UGA Botanical Gardens Callaway Building 4:00 pm - Talk 5:30 pm - Dinner