期刊
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
卷 39, 期 7, 页码 1318-1333出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/03610918.2010.491586
关键词
EM algorithm; Handwriting; Missing data; Mixed model; Multivariate functional data
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.
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