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This paper presents a method for the identi cation of \edge observations" located on a boundary areaconstructed by a truncation variable as well as for the identi cation of outliers and the after t of multivariateskew t -distribution(MST) to asymmetric data. The detection of edge observation is important in dataanalysis because it provides information on a certain critical area in observation space. The proposed methodis applied to an Australian Institute of Sport(AIS) dataset that is well known for asymmetry in data space.Keywords: Multivariate skew t -distribution, edge observation, outlier, ECM algorithm. 1.  Azzalini (1985)  Azzalini Dalla-Valle (1996)  MSN(multivariate skew normal distribu-tion)      .  MSN  MST(multivariate skew t -distribution)   .   (outliers)   .    MST    ,   . MST   . MST            .     \ (edge observations)"  .        .Sahu (2003)  MST . MST Lin (2010)  .  Pyne (2009)Sahu (2003)MST .  Lo (2008) Lo Gottardo (2012) Bickel Docksum (1981)Box-Cox   MST .    Lachos (2010) Cabral (2012) SNI (skew-normal/independent family)MST  .

Diagnosis of Observations after Fit of Multivariate Skew t-Distribution: Identification of Outliers and Edge Observations from Asymmetric Data