In many geodetic applications a large number of observations are being measured to estimate the unknown parameters. The unbiasedness property of the estimated parameters is only ensured if there is no bias (e.g. systematic effect) or falsifying observations, which are also known as outliers. One of the most important steps towards obtaining a coherent analysis for the parameter estimation is th...

Speeding describes the unusually fast responses provided to survey questions. A characteristic of speeders is that answers by-pass cognitive process. Consequently, this low respondent engagement results in the poor quality and validity of data. The issue at hand is how to detect speeders in a survey. The presumption is the use of different statistical outlier detection methods. This paper prese...

Outlier detection, also known as anomaly detection, is an important topic that has been studied for decades. An outlier detection system is able to identify anomalies in a dataset and thus improve data integrity by removing the detected outliers. It has been successfully applied to different types of data in various fields such as cyber-security, finance, and transportation. In the field of Mus...

Outlier detection is useful for credit card fraud detection. Due to drastic increase in digital frauds, there is a lot of financial losses and therefore various techniques are developed for fraud detection and applied to diverse business fields. In high-dimensional data, outlier detection presents some challenges because of increment of dimensionality. In this paper, the proposed model aims to ...

Abstract An outlier detection method may be considered fair over specified sensitive attributes if the results of are not skewed toward particular groups defined on such attributes. In this paper, we consider task detection. Our focus is multiple multi-valued (e.g., gender, race, religion, nationality and marital status, among others), one that has broad applications across modern data scenario...

Data collected by Wireless Sensor Networks (WSNs) are inherently unreliable. Therefore, to ensure high data quality, secure monitoring, and reliable detection of interesting and critical events, outlier detection mechanisms are needed to be in place. The constraint nature of resources available in WSNs necessities that unlike traditional outlier detection techniques performed off-line, outliers...