Principal component models for sparse functional data

@article{citeulike:2618657, abstract = {The elements of a multivariate dataset are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has complete measurements for each individual curve or, as is more common, one has measurements on a fine grid taken at the same time points for all curves, then many standard techniques may be applied. However, curves are often measured at an irregular and sparse set of time points which can differ widely across individuals. We present a technique for handling this more difficult case using a reduced rank mixed effects framework. 10.1093/biomet/87.3.587}, author = {James, G. M. and Hastie, T. J. and Sugar, C. A.}, citeulike-article-id = {2618657}, doi = {10.1093/biomet/87.3.587}, journal = {Biometrika}, keywords = {fda}, month = {September}, number = {3}, pages = {587--602}, posted-at = {2008-04-01 07:06:33}, priority = {2}, title = {Principal component models for sparse functional data}, url = {http://dx.doi.org/10.1093/biomet/87.3.587}, volume = {87}, year = {2000} }

TY - JOUR ID - citeulike:2618657 L3 - citeulike-article-id:2618657 N2 - The elements of a multivariate dataset are often curves rather than single points. Functional principal components can be used to describe the modes of variation of such curves. If one has complete measurements for each individual curve or, as is more common, one has measurements on a fine grid taken at the same time points for all curves, then many standard techniques may be applied. However, curves are often measured at an irregular and sparse set of time points which can differ widely across individuals. We present a technique for handling this more difficult case using a reduced rank mixed effects framework. 10.1093/biomet/87.3.587 IS - 3 JF - Biometrika EP - 602 TI - Principal component models for sparse functional data VL - 87 SP - 587 KW - fda AU - James, GM AU - Hastie, TJ AU - Sugar, CA PY - 2000/09/01/ UR - http://dx.doi.org/10.1093/biomet/87.3.587 ER -