Minimal Detectable and Identifiable Biases for quality control

Journal article (2018)

Authors

D. Imparato Curtin University
P.J.G. Teunissen Curtin University, Mathematical Geodesy and Positioning - CEG
C.C.J.M. Tiberius Mathematical Geodesy and Positioning - CEG
Research Group
Mathematical Geodesy and Positioning (CEG) (TU Delft)
Copyright: © 2018 D. Imparato, P.J.G. Teunissen, C.C.J.M. Tiberius
DOI
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https://doi.org/10.1080/00396265.2018.1437947
Global Navigation Satellite Systems (GNSS) Quality control Detection–identification–adaptation (DIA) Minimal Detectable Bias (MDB) Minimal Identifiable Bias (MIB)
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Published Date

01-03-2018

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Geoscience and Remote Sensing

Research Group

Mathematical Geodesy and Positioning

Abstract

The Minimal Detectable Bias (MDB) is an important diagnostic tool in data quality control. The MDB is traditionally computed for the case of testing the null hypothesis against a single alternative hypothesis. In the actual practice of statistical testing and data quality control, however, multiple alternative hypotheses are considered. We show that this has two important consequences for one's interpretation and use of the popular MDB. First, we demonstrate that care should be exercised in using the single-hypothesis-based MDB for the multiple hypotheses case. Second, we show that for identification purposes, not the MDB, but the Minimal Identifiable Bias (MIB) should be used as the proper diagnostic tool. We analyse the circumstances that drive the differences between the MDBs and MIBs, show how they can be computed using Monte Carlo simulation and illustrate by means of examples the significant differences that one can experience between detectability and identifiability.