A robust version of principal factor analysis View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2000

AUTHORS

G. Pison , P. J. Rousseeuw , P. Filzmoser , C. Croux

ABSTRACT

Our aim is to construct a factor analysis method that can resist the effect of outliers. We start with a highly robust initial covariance estimator, after which the factors can be obtained from maximum likelihood or from principal factor analysis (PFA). We find that PFA based on the minimum covariance determinant scatter matrix works well. We also derive the influence function of the PFA method. A new type of empirical influence function (EIF) which is very effective for detecting influential data is constructed. If the data set contains fewer cases than variables, we estimate the factor loadings and scores by a robust interlocking regression algorithm. More... »

PAGES

385-390

References to SciGraph publications

Book

TITLE

COMPSTAT

ISBN

978-3-7908-1326-5
978-3-642-57678-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-57678-2_51

DOI

http://dx.doi.org/10.1007/978-3-642-57678-2_51

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003661748


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