Journal of the American Statistical Association · 1985 · 111 citations · 0 references
ComputingData ScienceBayesian Data AnalysisStatistical ModelingTime Series AnalysisDimensionality ReductionMonte Carlo MethodsMultivariate AnalysisPrincipal Component AnalysisRobust Statistical ModelingRobust StatisticsCorrelation Matrix EstimationCovariance EstimationCovariance StructurePrincipal ComponentsProjection PursuitRobust Covariance EstimationRobust EstimationRobust Principal Component Analysis
Abstract This article proposes and discusses a type of new robust estimators for covariance/correlation matrices and principal components via projection-pursuit techniques. The most attractive advantage of the new procedures is that they are of both rotational equivariance and high breakdown point. Besides, they are qualitatively robust and consistent at elliptic underlying distributions. The Monte Carlo study shows that the best of the new estimators compare favorably with other robust methods. They provide as good a performance as M-estimators and somewhat better empirical breakdown properties. Key Words: Covariance matrixCorrelation matrixBreakdown pointRotational equivarianceMultivariate data analysisConsistency