The process of detection of outliers is an interesting and important aspect in the analysis of data, as it could impact the inference. Literature is abundant with procedures for detection and testing of single outliers in sample data. However, the presence of two or more outliers in multivariate data would render the detection and testing process more complicated as majority of outliers are invisible to many of the methods. This is due to the masking effect, and regular classical and related methods being found unsuitable for use of outlier identification techniques. The difficulty of detection increases with the number of outliers and the dimension of the data because the outliers can be extreme in any growing number of directions. An overview of multivariate outlier detection methods are provided in this study because of its growing importance in a wide variety of practical situations.
How to Cite:
Sajesh, T.A. and Srinivasan, M.R., 2013. An Overview of Multiple Outliers in Multidimensional Data. Sri Lankan Journal of Applied Statistics, 14(2), pp.87–120. DOI: http://doi.org/10.4038/sljastats.v14i2.6214
Sajesh, T Aand M R Srinivasan. “An Overview of Multiple Outliers in Multidimensional Data”. Sri Lankan Journal of Applied Statistics, vol. 14, no. 2, 2013, pp. 87–120. DOI: http://doi.org/10.4038/sljastats.v14i2.6214