Preprint Article Version 1 This version is not peer-reviewed

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

Version 1 : Received: 24 January 2020 / Approved: 25 January 2020 / Online: 25 January 2020 (16:40:16 CET)

A peer-reviewed article of this Preprint also exists.

Rofatto, V.F.; Matsuoka, M.T.; Klein, I.; Roberto Veronez, M.; da Silveira, L.G., Jr. A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis. Remote Sens. 2020, 12, 860. Rofatto, V.F.; Matsuoka, M.T.; Klein, I.; Roberto Veronez, M.; da Silveira, L.G., Jr. A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis. Remote Sens. 2020, 12, 860.

Journal reference: Remote Sens. 2020, 12, 860
DOI: 10.3390/rs12050860

Abstract

An iterative outlier elimination procedure based on hypothesis testing, commonly known as Iterative Data Snooping (IDS) among geodesists, is often used for the quality control of the modern measurement systems in geodesy and surveying. The test statistic associated with IDS is the extreme normalised least-squares residual. It is well-known in the literature that critical values (quantile values) of such a test statistic cannot be derived from well-known test distributions, but must be computed numerically by means of Monte Carlo. This paper provides the first results about Monte Carlo-based critical value inserted to different scenarios of correlation between the outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, overidentifications and statistical overlap associated with IDS in the presence of a single outlier. Based on such probability levels we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for the case where IDS is in play. MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances that the larger the Type I decision error (smaller critical value), the higher the rates of outlier detection, but the lower the rates of outlier identification. For that case, the larger the Type I Error, the larger the ratio between MIB and MDB. We also highlight that an outlier becomes identifiable when the contribution of the measures to the wrong exclusion rate decline simultaneously. In that case, we verify that the effect of the correlation between the outlier statistics on the wrong exclusion rates becomes insignificant from a certain outlier magnitude, which increases the probability of identification.

Subject Areas

Probability; Hypothesis Testing; Outlier Detection; Monte Carlo; Quality Control; Control System; Reliability; Sensitivity; Random Number Generators

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