Gaussian imputation uses the classical non-robust mean and covariance estimator and then imputes predictions under the multivariate normal model. Outliers may be created by this procedure. Then a high-breakdown robust estimate of the location and scatter with the Minimum Covariance Determinant algorithm is obtained and finally outliers are determined based on Mahalanobis distances based on the robust location and scatter.

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`data` |
a data frame or matrix with the data |

`alpha` |
a threshold value for the cut-off for the outlier Mahalanobis distances |

`seedem` |
random number generator seed for EM algorithm, default is 234567819 |

`seedmcd` |
random number generator seed for MCD algorithm, if seedmcd is missing an internal seed will be used. |

Normal imputation from package `norm`

and MCD from package `MASS`

. Note that currently MCD does not accept weights.

Result is stored in a global list GIMCD.r:

`center` |
robust center |

`scatter` |
robust covariance |

`alpha` |
Quantile for cut-off value |

`computation.time` |
Elapsed computation time |

`outind` |
logical vector of outlier indicators |

`dist` |
Mahalanobis distances |

Beat Hulliger

B\'eguin, C. and Hulliger, B. (2008) The BACON-EEM Algorithm for Multivariate Outlier Detection
in Incomplete Survey Data, *Survey Methodology*, Vol. 34, No. 1, pp. 91-103.

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