Detection of Outliers in Multivariate Data using Minimum Vector Variance Method

Abstract

Outliers are observations that do not follow the distribution of data patterns and can cause deviations from data analysis, so a method for identifying outliers is needed One method in scanning detection is Minimum Vector Variance which is a robust estimator that uses the minimum Vector Variance (VV) criteria. In this study, the MVV method was used to detect outliers in criminality data in Indonesia in 2013 and data that had been entered out by 5% and 10%. The results showed that the MVV method was more effective than the Mahalanobis distance when detecting outliers in data that had been entered out by 5% and 10%.

Outlier Detection on High Dimensional Data Using Minimum Vector Variance (Mvv)

“…However, in its application to the principal component analysis, the CD has limitations if its value is close to zero or equal to zero. Therefore, a new concept for solving this problem was launched, known as vector variance (VV) or defined as Tr(Σ 2 ) [3].…”

“…Outliers are part of the data from a data set that does not follow the data distribution pattern and is located far from the data center. Outliers in the data can result in inaccurate data analysis results, such as deviations from statistical test results based on the mean and covariance parameters [3]. Therefore, detection of outlier indications is needed, especially in extensive data.…”

“…However, the Mahalanobis distance is still included in the classical estimator, which relies on basic assumptions such as normality, linearity, etc. Therefore, a robust method is needed against outliers [3] . One alternative method has been discussed to obtain a robust Mahalanobis distance in detecting outliers in multivariate data, including [3] , [7] , [8], and [9], using the Minimum Vector Variance (MVV) method.…”

“…Therefore, a robust method is needed against outliers [3] . One alternative method has been discussed to obtain a robust Mahalanobis distance in detecting outliers in multivariate data, including [3] , [7] , [8], and [9], using the Minimum Vector Variance (MVV) method. However, in this discussion, the multivariate data used is not classified as high-dimensional data with p greater than n. The MVV method utilizes the total variance in finding the minimum covariance matrix so that a robust Mahalanobis distance can be obtained against the outliers.…”

Outlier Detection Using Minimum Vector Variance Algorithm with Depth Function and Mahalanobis Distance

“…Keberadaan pencilan pada data dapat mengakibatkan penyimpangan terhadap hasil analisis data dan menyebabkan ketidak akuratan hasil analisis dalam penelitian, oleh karena itu salah satu solusinya adalah dengan mendeteksi adanya pencilan pada data [7]. Konsep dasar dalam pendeteksian outlier pada data multivariat adalah dengan mengukur jarak setiap titik ke pusat datanya [8]. Titik sampel dikatakan pencilan jika memiliki nilai jarak yang relatif lebih besar dibanding mayoritas sampel yang lain.…”