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.

• Type: Journal
• Year: 2006
• Where: CSDA
• Authors: Paul H. C. Eilers, Iain D. Currie, María Durbán
• Keywords: Algorithm, CSDA 2006, Penalized Fisher, Tensor Product