Power of Global Test in Deformation Analysis

Aydin, Cüneyt

doi:10.1061/(asce)su.1943-5428.0000064

Cited by 41 publications

(26 citation statements)

References 2 publications

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“…The MDB in Equations (13) or (15) of an alternative hypothesis is the smallest-magnitude outlier that can lead to the rejection of the null hypothesis H 0 for a given α 0 and β 0 . Thus, for each model of the alternative hypothesis H (i) A , the corresponding MDB can be computed [12,49,65]. The limitation of this MDB is that it was initially developed for the binary hypothesis testing case.…”

Section: (I)mentioning

confidence: 99%

“…The Monte Carlo method provides insights into these cases, in which analytical solutions are too complex to fully understand, are doubted for one reason or another or are not available [12]. The Monte Carlo method for quality control purposes has already been applied in geodesy (see, e.g., [2,10,22,23,33,46,[48][49][50][51]). For in-depth coverage of Monte Carlo methods, consult, for instance, [52][53][54].…”

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confidence: 99%

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A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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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 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 on the Monte Carlo-based critical value inserted into different scenarios of correlation between outlier statistics. From the Monte Carlo evaluation, we compute the probabilities of correct identification, missed detection, wrong exclusion, over-identifications and statistical overlap associated with IDS in the presence of a single outlier. On the basis of such probability levels, we obtain the Minimal Detectable Bias (MDB) and Minimal Identifiable Bias (MIB) for cases in which IDS is in play. The MDB and MIB are sensitivity indicators for outlier detection and identification, respectively. The results show that there are circumstances in which 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. In such a case, the larger the Type I Error, the larger the ratio between the MIB and MDB. We also highlight that an outlier becomes identifiable when the contributions of the measures to the wrong exclusion rate decline simultaneously. In this case, we verify that the effect of the correlation between outlier statistics on the wrong exclusion rate becomes insignificant for a certain outlier magnitude, which increases the probability of identification.

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Section: (I)mentioning

confidence: 99%

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confidence: 99%

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

et al. 2020

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“…If a 1-dimensional test is performed and the size is chosen as = 0.05 and the test power 1 − = 0.80, = 2, a value of noncentrality parameter in ( ) is = 32 (Aydin, 2012).…”

Section: Geometric Deformation Analysis In Free Geodetic Network: Camentioning

confidence: 99%

“…To establish a measure for the quality of the monitoring network, the sensitivity analysis is established (Pelzer, 1971;Aydin, 2012). In the context of deformation analysis, we shall talk about the minimal detectable deformation (MDD) (Prószyński, 2010;Velsink, 2015).…”

Section: Minimal Detectable Deformationmentioning

confidence: 99%

Geometric deformation analysis in free geodetic networks: case study for Fruska Gora in Serbia

Sušić¹

2017

Acta Geodynamica Et Geomaterialia

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“…is the pooled variance factor estimator for both epochs and (e) , α is the significance level, i.e., the probability of making a Type I error in testing statistical, u = rank(Qd R ) and F is the critical value read from Fisher's cumulative distribution function (Aydin 2013). If the global test passes, this means that alld R i vectors are not statistically significant and, thus, these points can be regarded as actually stable.…”

Section: 3mentioning

confidence: 99%

Robust estimation of deformation from observation differences for free control networks

Nowel

Kamiński

2014

J Geod

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Deformation measurements have a repeatable nature. This means that deformation measurements are performed often with the same equipment, methods, geometric conditions and in a similar environment in epochs 1 and 2 (e.g., a fully automated, continuous control measurements). It is, therefore, reasonable to assume that the results of deformation measurements can be distorted by both random errors and by some non-random errors, which are constant in both epochs. In other words, there is a high probability that the difference in the accuracy and precision of measurement of the same geometric element of the network in both epochs has a constant value and sign. The constant errors are understood, but the manifestation of these errors is difficult to determine in practice. For free control networks (the group of potential reference points in absolute control networks or the group of potential stable points in relative networks), the results of deformation measurements are most often processed using robust methods. Classical robust methods do not completely eliminate the effect of constant errors. This paper proposes a new robust alternative method called REDOD. The performed tests showed that if the results of deformation measurements were additionally distorted by constant errors, the REDOD method completely eliminated their effect from deformation analysis results. If the results of deformation measurements are only distorted by random errors, the REDOD method yields very similar deformation analysis results as the classical IWST method. The numerical tests were preceded by a theoretical part. The theoretical part K. Nowel (B) · W. Kamiński Institute of Geodesy, University of Warmia and Mazury in Olsztyn, 1 Oczapowskiego Str., 10-719 Olsztyn, Poland e-mail: krzysztof.nowel@uwm.edu.pl W. Kamiński e-mail: waldemar.kaminski@uwm.edu.pl describes the algorithm of classical robust methods. Particular attention was paid to the IWST method. In relation to classical robust methods, the optimization problem of the new REDOD method was formulated and the algorithm for its solution was derived.

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Power of Global Test in Deformation Analysis

Cited by 41 publications

References 2 publications

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

A Monte Carlo-Based Outlier Diagnosis Method for Sensitivity Analysis

Geometric deformation analysis in free geodetic networks: case study for Fruska Gora in Serbia

Robust estimation of deformation from observation differences for free control networks

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