Testing normality of chosen R-estimates used in deformation analysis

Duchnowski, Robert; Wyszkowska, Patrycja

doi:10.1515/jogs-2020-0002

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…HLEs and HLWEs require that the model of equation ( 9) be univariate. However, if each parameter in the multivariate model is examined and estimated separately, those estimates can also be applied to more complex geodetic networks [61,122,123].…”

Section: R-estimationmentioning

confidence: 99%

Robust procedures in processing measurements in geodesy and surveying: a review

Duchnowski,

Wyszkowska

2024

Meas. Sci. Technol.

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Outlying observations are undesirable but possible elements of geodetic measurements. In such a context, the primary and trivial solution is to repeat “suspected” observations. The question arises: what if the measurements cannot be performed again, or if one cannot flag outliers easily and efficiently? In such a case, one should process data by applying methods that consider the possible occurrence of outlying observations. Historically, except for some previous attempts, the statistical approach to robust estimation originates in the 60s of the 20th century and refers to the pioneer papers of Huber, Tukey, Hampel, Hodges, and Lehmann. Also, the statistical procedures known as data snooping (data dredging) were developed at a similar time. It took not a long time before robust procedures were implemented for processing geodetic observations or adjustment of observation systems. The first works of Baarda and Pope encouraged other scientists or surveyors to elaborate robust procedures adapted for geodetic or surveying problems, which resulted in their rapid development in the last two decades of the 20th century. The question for the 21st century is whether robustness is still an important issue relating to modern measurement technologies and numerical data processing. One should realize that modern geodetic techniques do not decrease the probability of outlier occurrence. Considering measurement systems that yield big data, it is almost certain that outliers occur somewhere. The paper reviews different approaches to robust processing of geodetic observations, from the data snooping methods, random sampling, M-estimation, R-estimation, and Msplit estimation to robust estimation of the variance coefficient. Such a variety reflects different natures, origins, or properties of outliers and the apparent fact that there is no best and most efficient and universal robust approach. The methods presented are indeed the basis for future solutions based on, e.g., machine learning.

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Section: R-estimationmentioning

confidence: 99%

Robust procedures in processing measurements in geodesy and surveying: a review

Duchnowski,

Wyszkowska

2024

Meas. Sci. Technol.

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“…The sample mentioned is created for each parameter X j separately. In the paper context, the sample elements are created by computing the coordinates of a particular network point by applying the raw observations and the reference point coordinates in all possible independent ways (Duchnowski, 2013(Duchnowski, , 2021. M split estimation is the last method considered here.…”

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confidence: 99%

Point displacements during classical measurements – a practical approach to pseudo epochs between measurements

Duchnowski

Wyszkowska

2022

Proceedings of the 5th Joint International Symposium on Deformation Monitoring - JISDM 2022

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Various measurement techniques and data processing are applied to determine point displacements and deformation of geodetic networks or buildings. Considering classical measurements and analysis of the network deformation, we should realize that the measurements are not “immediate.” The question arises: what happens if a point (or some points) displaces between particular measurements within one epoch. In such a case, the observation set would consist of the observations before and after point displacement, and such hypothetical observation groups can be regarded as related to two (or more) pseudo epochs. The paper's main objective is to examine some estimation methods that would probably deal with such a problem, namely Msplit estimation (in two variants, the squared and the absolute Msplit estimation) and chosen robust methods, namely Huber’s method (example M-estimation) and the Hodges-Lehmann weighted estimation (basic R-estimation). The first approach can provide two (or more) variants of the network point coordinates (here, before and after point movements), providing information about two (or more) states of the network during measurements. In contrast, the robust methods can only decrease the influence of the outliers on the computed network point coordinates. Thus, estimation results would concern only one network state in such a case. The presented empirical analyses show that the better and more realistic results are obtained by applying Msplit estimation. Huber’s method can also provide acceptable results (describing the network state at the epoch beginning) only if the number of observations conducted after the point displacements is not too high.

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Editorial to the Special Issue on the International Symposium on Applied Geoinformatics 2019

Paziewski

Şanli

2021

Journal of Geodetic Science

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Testing normality of chosen R-estimates used in deformation analysis

Cited by 3 publications

References 20 publications

Robust procedures in processing measurements in geodesy and surveying: a review

Robust procedures in processing measurements in geodesy and surveying: a review

Point displacements during classical measurements – a practical approach to pseudo epochs between measurements

Editorial to the Special Issue on the International Symposium on Applied Geoinformatics 2019

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