Outlier detection in skewed data

In this work we develop two new algorithms for outlier detection in skewed data. The first algorithm uses an adjusted median with the help of Robust Support Vector Regression and the second one estimates a robust covariance matrix and incorporates it in the Kernel Density Estimation of the data. We applied these two methods in outlier detection in univariate skewed and multivariate skewed data and show that they succed in detecting the outliers and also in including more inliers. Based on simulation study it is realized that the proposed procedures have the advantage in skewed data. Finally, both methods are also used in detecting MRIs of patients diagnosed with dementia by measuring the outlyingness from the MRIs of nondemented patients.