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CSDA
2006

Fast and compact smoothing on large multidimensional grids

10 years 1 months ago
Fast and compact smoothing on large multidimensional grids
A framework of penalized generalized linear models and tensor products of B-splines with roughness penalties allows effective smoothing of data in multidimensional arrays. A straightforward application of the penalized Fisher scoring algorithm quickly runs into storage and computational difficulties. A novel algorithm takes advantage of the special structure of both the data as an array and the model matrix as a tensor product; the algorithm is fast, uses only a moderate amount of memory and works for any number of dimensions. Examples are given of how the method is used to smooth life tables and image data.
Paul H. C. Eilers, Iain D. Currie, María Du
Added 11 Dec 2010
Updated 11 Dec 2010
Type Journal
Year 2006
Where CSDA
Authors Paul H. C. Eilers, Iain D. Currie, María Durbán
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