A Robust Version of Principal Factor Analysis
نویسندگان
چکیده
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.
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