Estimation of functional principal components by the kernel method
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2013, Vol 6, Issue 2
Abstract
The principal components analysis (PCA) is one of the important methods in multivariate and functional data analysis to reduce the dimension of problems. PCA is a mathematical procedure that use an orthogonal transform for transforming set of corre- lated variables into the set that contains amounts of random uncorrelated variables, called principal components. Despite the classical PCA, in functional PCA we need to smooth the estimated principal components curves. In this paper, we prepare the smooth kernel base estimators for functional PCA whenever the data is continuous path from stochastic processes.
Authors and Affiliations
Akbar Abravesh| Department of Mathematical Sciences,Mohaghegh Ardabili University, Ardabil, Iran, Behdad Mostafaiy| Department of Mathematical Sciences,Mohaghegh Ardabili University, Ardabil, Iran
Keywords
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