The objective of this research is detection of outliers in multivariate data employing various distance measure, particularly using robust regression diagnosis technique. Several classical outlier identification methods are based on the sample mean and covariance matrix in general. But they do not always yield better result, as they themselves are affected by the outliers. Sometimes one outlier point has hide the other outliers. To identify them, methods which have masking effect with outlier points are being used. An appropriate method is adopted to identify the unmasking outliers and also to compare the various distance measures. Mathematics Subject Classification: 62H99, 62J05

/pdf/outlier-detection-in-multivariate-data-zsnw3f12t4.pdf

Outlier detection in multivariate data

This research demonstrates the adverse implications of using non-robust statistical methods for detecting anomalies in hyperspectral image data, and proposes the use of multivariate outlier detection methods as an alternative detection strategy. Existing outlier detection methods are adapted for use in a hyperspectral image context, and their performance is compared to the benchmark RX detector and a cluster-based anomaly detector. Tests conducted using both simulated data and actual hyperspectral imagery indicate that multivariate outlier detection methods can achieve superior detection performance relative to current non-robust detection methods.

Finding Hyperspectral Anomalies Using Multivariate Outlier Detection

Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical research. However, one source of HD data that has received relatively little attention is functional magnetic resonance images (fMRI), which consists of hundreds of thousands of measurements sampled at hundreds of time points. At a time when the availability of fMRI data is rapidly growing-primarily through large, publicly available grassroots datasets-automated quality control and outlier detection methods are greatly needed. We propose principal components analysis (PCA) leverage and demonstrate how it can be used to identify outlying time points in an fMRI run. Furthermore, PCA leverage is a measure of the influence of each observation on the estimation of principal components, which are often of interest in fMRI data. We also propose an alternative measure, PCA robust distance, which is less sensitive to outliers and has controllable statistical properties. The proposed methods are validated through simulation studies and are shown to be highly accurate. We also conduct a reliability study using resting-state fMRI data from the Autism Brain Imaging Data Exchange and find that removal of outliers using the proposed methods results in more reliable estimation of subject-level resting-state networks using independent components analysis.

/pdf/pca-leverage-outlier-detection-for-high-dimensional-4n3gpvhbpw.pdf

PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data.

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. DOI:  http://dx.doi.org/10.4038/sljastats.v14i2.6214

/pdf/an-overview-of-multiple-outliers-in-multidimensional-data-rlccmbugj3.pdf

An Overview of Multiple Outliers in Multidimensional Data

This work presents an analysis based on outliers detection on multivariate dataset of sensor networks. Initially, we characterize the outliers detection problem in these networks. Then, three general methods for outliers detection methods Minimum Volume Ellipsoid (MVE), Minimum Covariance Determinant (MCD) and Max-Eigen Difference (MED) were used and evalueted. The dataset used in the simulation was generated from an air pollutants dataset. This generation allowed the use of this dataset in sensor networks scenarios. The phenomenon has characterized by a Normal distribution. To represent the sensor perception fails, two different distributions was used, the Skew-Normal and T-Student. In addition, the sensor noise was inserted by using a Bernoulli process. The data representativeness, after the outlier removal, was performed by statistical tools: the absolute relative error, ANOVA, measures of central tendency and the number of outliers. The simulations were performed by software R. The results showed that the MVE and MCD can be used satisfatory in general sensor networks applications. The MED does not remove all outliers, so, its usage is not recommended in these applications

/pdf/deteccao-de-outliers-multivariados-em-redes-de-sensores-ko0nnthyst.pdf

Detecção de outliers multivariados em redes de sensores.

This article proposes a new procedure named Max-Eigen difference (MED) for identifying outliers in multivariate data sets. Theoretical aspects of the procedure are briefly discussed. The proposed procedure is compared with the Mahalanobis distance (MD) and robust distance (RD) via two examples. It is indicated that the MED works better than MD and is comparable with RD. Finally, this procedure is applied during constructing a quadratic discriminant analysis which is used to splicing sites prediction for DNA sequences. Through the results of rice and human genome data sets, it can be seen that the robustified discriminant provides higher prediction accuracy than the usual discrimination method.

A New Approach for Detecting Multivariate Outliers

Secrecy Rate Analysis for RIS-Aided Multi-User MISO System over Rician Fading Channel

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A New Approach for Detecting Multivariate Outliers

Secrecy Rate Analysis for RIS-Aided Multi-User MISO System over Rician Fading Channel