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Open AccessArticle

A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested

1
Department of Physics and Chemistry, Technical University of Cluj-Napoca, 400641 Cluj, Romania
2
Chemical Doctoral School, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania
Symmetry 2019, 11(6), 835; https://doi.org/10.3390/sym11060835
Received: 12 June 2019 / Revised: 21 June 2019 / Accepted: 24 June 2019 / Published: 25 June 2019
(This article belongs to the Special Issue Symmetry in Applied Mathematics)
One of the pillars of experimental science is sampling. Based on the analysis of samples, estimations for populations are made. There is an entire science based on sampling. Distribution of the population, of the sample, and the connection among those two (including sampling distribution) provides rich information for any estimation to be made. Distributions are split into two main groups: continuous and discrete. The present study applies to continuous distributions. One of the challenges of sampling is its accuracy, or, in other words, how representative the sample is of the population from which it was drawn. To answer this question, a series of statistics have been developed to measure the agreement between the theoretical (the population) and observed (the sample) distributions. Another challenge, connected to this, is the presence of outliers - regarded here as observations wrongly collected, that is, not belonging to the population subjected to study. To detect outliers, a series of tests have been proposed, but mainly for normal (Gauss) distributions—the most frequently encountered distribution. The present study proposes a statistic (and a test) intended to be used for any continuous distribution to detect outliers by constructing the confidence interval for the extreme value in the sample, at a certain (preselected) risk of being in error, and depending on the sample size. The proposed statistic is operational for known distributions (with a known probability density function) and is also dependent on the statistical parameters of the population—here it is discussed in connection with estimating those parameters by the maximum likelihood estimation method operating on a uniform U(0,1) continuous symmetrical distribution. View Full-Text
Keywords: test for outliers; order statistics; extreme values; confidence intervals; Monte-Carlo simulation test for outliers; order statistics; extreme values; confidence intervals; Monte-Carlo simulation
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Jäntschi, L. A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested. Symmetry 2019, 11, 835.

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