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  "Title": "Multivariate Outlier Detection and Imputation for Incomplete\nSurvey Data",
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  "Description": "Algorithms for multivariate outlier detection when missing\nvalues occur. Algorithms are based on Mahalanobis distance or\ndata depth. Imputation is based on the multivariate normal\nmodel or uses nearest neighbour donors. The algorithms take\nsample designs, in particular weighting, into account. The\nmethods are described in Bill and Hulliger (2016)\n<doi:10.17713/ajs.v45i1.86>.",
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