In this paper the use of outlier detection methods is discussed. This analysis is an introduction to the use of various methods of outlier detection in medical diagnoses (screening). The authors investigated the usefulness of selected outlier detection methods in the context of detection sensitivity, speed performance analysis and the difficulty of automating the performance analysis by using t...

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...

Classical methods for detecting outliers deal with continuous variables. These methods are not readily applicable to categorical data, such as incorrect/correct scores (0/1) and ordered rating scale scores (e.g., 0, . . . , 4) typical of multi-item tests and questionnaires. This study proposes two definitions of outlier scores suited for categorical data. One definition combines information on ...

The purpose of this article is to explicate the procedures for detecting outliers subject in bioequivalence study as per as per various regulatory agencies (e.g. USFDA, TPD, ANVISA and CDSCO). The existence of outlier subjects in bioequivalence studies possibly will have an effect on the results of the study. The present article provides the details regarding Studentized Residual Test and Lund ...

We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second test determines the type of additive outlier (volatility or level). The tests are shown to be similar wi...

Four outlier detection methods are compared using both publicly available smaller statistical datasets and real-life Knowledge Discovery in Databases (KDD) datasets [1]. The smaller datasets provide insight (via visualisations) into the relative strengths and weaknesses of the compared methods. The real-life large datasets test scalability and practicality of application. We are unaware of prev...

Increased leakage and process variations make distinction between fault-free and faulty chips by IDDQ test difficult. Earlier the concept of Current Ratios (CR) was proposed to screen defective (outlier) chips. However, it is not capable of catching certain defects. Neighboring chips on a wafer have similar fault-free parameters that are correlated through the underlying fabrication process. Ba...

Outlier detection is an integral part of robust evaluation for crowdsourceable Quality of Experience (QoE) and has attracted much attention in recent years. In QoE for multimedia, outliers happen because of different test conditions, human errors, abnormal variations in context, etc. In this paper, we propose a simple yet effective algorithm for outlier detection and robust QoE evaluation named...