MCD: RATS module to compute robust mean vector and covariance matrix
Given n joint observations on k continuous variables, mcd.src computes a robust mean vector and a robust covariance matrix using the minimum covariance determinant algorithm [P. J. Rousseeuw and A. M. Leroy (1987), Robust Regression and Outlier Detection, New York: Wiley]. Observations whose robust Mahalanobis distances exceed the 97.5% chi-square value with k degrees of freedom are flagged as potential outliers. mcd.src uses a resampling method, and the number of subsamples (each having k+1 data) is the procedure's only option (default = 3000 subsamples). mcd.src reads the data as series whose first observation is start and whose last observation is end (i. e., n = end - start + 1).
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