Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 39, Issue 7, Pages 1318-1333Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2010.491586
Keywords
EM algorithm; Handwriting; Missing data; Mixed model; Multivariate functional data
Categories
Ask authors/readers for more resources
This article introduces principal component analysis for multidimensional sparse functional data, utilizing Gaussian basis functions. Our multidimensional model is estimated by maximizing a penalized log-likelihood function, while previous mixed-type models were estimated by maximum likelihood methods for one-dimensional data. The penalized estimation performs well for our multidimensional model, while maximum likelihood methods yield unstable parameter estimates and some of the parameter estimates are infinite. Numerical experiments are conducted to investigate the effectiveness of our method for some types of missing data. The proposed method is applied to handwriting data, which consist of the XY coordinates values in handwritings.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available