Portland State University
Fariborz Maseeh Department of Mathematics & Statistics
The Maseeh Mathematics and Statistics Colloquium Series presents
Raymond Carroll, Distinguished Professor of Statistics, Nutrition and Toxicology, Department of Statistics, Texas A&M University
Deconvolution and Classification
In a series of papers on Lidar data, magically good classification rates are claimed once data are deconvolved and a dimension reduction technique applied. The latter can certainly be useful, but it is not clear a priori that deconvolution is a good idea in this context. After all, deconvolution adds noise, and added noise leads to lower classification accuracy. I will give a more or less formal argument that in a closely related class of deconvolution problems, what statisticians call "Measurement Error Models", deconvolution leads to increased classification error rates. An empirical example in a more classical deconvolution context illustrates the results.
Friday, April 29, 3:15 p.m.
Neuberger Hall room 454
(Refreshments served at 3:00 Neuberger Hall room 344)
* Sponsored by the Maseeh Mathematics and Statistics Colloquium Series Fund and the Fariborz Maseeh Department of Mathematics & Statistics, PSU. This event is free and open to the public.