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Data Snooping is the most best-established method for identifying outliers in geodetic data analysis. It has been demonstrated in the literature that to effectively user-control the type I error rate, critical values must be computed numerically by means of Monte Carlo. Here, on the other hand, we provide a model based on an artificial neural network. The results prove that the proposed model can be used to compute the critical values and, therefore, it is no longer necessary to run the Monte Carlo-based critical value every time the quality control is performed by means of data snooping.

An artificial neural network-based critical values for multiple hypothesis testing: data-snooping case