Estimating Average Variation About the Population Mean Using Geometric Measure of Variation

Troon, Benedict

doi:10.31730/osf.io/avqw2

Cited by 1 publication

(1 citation statement)

References 2 publications

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“…The next task now is to calculate the geometric averages with respect to the mean, this is given as, the study borrowed a concept from [24] = ∏ − While formulating the G, the study established that most important is the deviation from the median can either take a positive, a negative or a zero value, making the formula not applicable in an event we get a negative value since we cannot get a real root of a negative number. In response to this shortcoming, the study took the absolute of the deviations given the rule of geometric averaging holds that most important is the magnitude of the deviation and not the direction [25,26].…”

Section: Methodsmentioning

confidence: 99%

Outlier Detection Technique for Univariate Normal Datasets

Owuor¹,

Benedict²,

Kevin³

2022

AJTAS

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This paper presents an outlier detection technique for univariate normal datasets. Outliers are observations that lips an abnormal distance from the mean. Outlier detection is a useful technique in such areas as fraud detection, financial analysis, health monitoring and Statistical modelling. Many recent approaches detect outliers according to reasonable, predefined concepts of an outlier. Methods of outlier detection such as Gaussian method of outlier detection have been widely used in the detection of outliers for univariate data-sets, however, such methods use measure of central tendency and dispersion that are affected by outliers hence making the method to be less robust towards detection of outliers. The study aimed at providing an alternative method that can be used in outlier detection for univariate normal data sets by deploying the measures of variation and central tendency that are least affected by the outliers (median and the geometric measure of variation). The study formulated an outlier detection formula using median and geometric measure of variation and then applied the formulation on randomly simulated normal dataset with outliers and recorded the number of outliers detected by the method in comparison to the other two existing best methods of outlier detection. The study then compared the sensitivity of the three methods in outlier detection. The simulation was done in two different ways, the first considered the variation in mean with a constant standard deviation while the second test held the mean constant while varying the standard deviation. The formulated outlier detection technique performed the best, eliminating the most required number of outliers compared to other two Gaussian outlier detection techniques when there was variation in mean. The study also established that the formulated method of outlier detection was stricter when the standard deviation was varied but still stands out to be the best as an outlier is defined relative to the mean and not the standard deviation. The study established that the formulated method is more sensitive than the Gaussian Method of outlier detection but performed as well as the best existing outlier detection technique. In conclusion, the study established that the formulated method could be employed in outlier detections for univariate normal data-sets as it performed almost the same to the best existing method of outlier detection for univariate data-sets.

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Section: Methodsmentioning

confidence: 99%