Abstract
The principal component analysis (PCA) is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit (PP), a more general class of dimension-reduction techniques. However, the application of this extended procedure is often hampered by its complexity in computation and by lack of some appropriate theory. In this paper, by use of the empirical processes we established a large sample theory for the robust PP estimators of the principal components and dispersion matrix.
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The research was partially supported by the National Natural Science Foundation of China under Grant No. 19631040.
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Zhang, J. Projection-Pursuit Based Principal Component Analysis: a Large Sample Theory. Jrl Syst Sci & Complex 19, 365–385 (2006). https://doi.org/10.1007/s11424-006-0365-0
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DOI: https://doi.org/10.1007/s11424-006-0365-0