Graph-based two-sample tests for data with repeated observations

Low-altitude unmanned aerial vehicle (UAV) photogrammetry combined with structure-from-motion (SFM) algorithms is the latest technological approach to imaging 3D stereo constructions. At present, derivative products have been widely used in landslide monitoring, landscape evolution, glacier movement, volume measurement, and landscape change detection. However, there is still a lack of research into the accuracy of 3D data positioning based on the structure-from-motion of unmanned aerial vehicle (UAV-SFM) technology, itself, which can affect the measurable effectiveness of the results in further applications of this technological approach. In this paper, validation work was carried out for the DJI Phantom 4 RTK UAV, for earth observation data related to 3D positioning accuracy. First, a test plot with a relatively stable surface was selected for repeated flight imaging observations. Specifically, three repeated flights were performed on the test plot to obtain three sorties of images; the structure from motion and multi-view stereo (SFM-MVS) key technology was used to process and construct a 3D scene model, and based on this model the digital surface model (DSM) and digital orthophoto map (DOM) data of the same plot with repeated observations were obtained. In order to check the level of 3D measurement accuracy of the UAV technology itself, a window selection-based method was used to sample the point cloud set data from the three-sortie repeat observation 3D model. The DSM and DOM data obtained from three repeated flights over the surface invariant test plots were used to calculate the repeat observation 3D point errors, taking into account the general methodology of redundant observation error analysis for topographic surveys. At the same time, to further analyze the limits of the UAV measurement technique, possible under equivalent observation conditions with the same processing environment, a difference model (DOD) was constructed for the DSM data from three sorties, to deepen the overall characterization of the differences between the DSMs obtained from repeated observations. The results of the experimental study concluded that both the analysis of the 3D point set measurements based on window sampling and the accuracy evaluation using the difference model were generally able to achieve a centimeter level of planimetric accuracy and vertical accuracy. In addition, the accuracy of the surface-stabilized hardened ground was better, overall, than the accuracy of the non-hardened ground. The results of this paper not only probe the measurement limits of this type of UAV, but also provide a quantitative reference for the accurate control and setting of an acquisition scheme of the UAV-based SfM-MVS method for geomorphological data acquisition and 3D reconstruction.

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ANTHROPOGENIC INFLUENCE OF THE KOMSOMOLSK OIL AND GAS CONDENSATE FIELD ON MODERN DEFORMATION PROCESSES

Oil and Gas Studies ◽

10.31660/0445-0108-2018-2-11-20 ◽

2018 ◽

pp. 11-20 ◽

Cited By ~ 1

Author(s):

Yu. V. Vasilev ◽

D. A. Misyurev ◽

A. V. Filatov

Keyword(s):

Oil And Gas ◽

Gas Production ◽

Gas Condensate ◽

Industrial Safety ◽

Subsidence Trough ◽

Oil And Gas Production ◽

Field Development ◽

Repeated Observations ◽

Deformation Processes ◽

The Relationship

The authors created a geodynamical polygon on the Komsomolsk oil and gas condensate field to ensure the industrial safety of oil and gas production facilities. The aim of its creation is mul-tiple repeated observations of recent deformation processes. Analysis and interpretation of the results of geodynamical monitoring which includes class II leveling, satellite observations, radar interferometry, exploitation parameters of field development provided an opportunity to identify that the conditions for the formation of recent deformations of the earth’s surface is an anthropogenic factor. The authors identified the relationship between the formation of subsidence trough of the earth’s surface in the eastern part of the field with the dynamics of accumulated gas sampling and the fall of reservoir pressures along the main reservoir PK1 (Cenomanian stage).

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No one accelerometer-based physical activity data collection protocol can fit all research questions

10.21203/rs.2.11020/v2 ◽

2019 ◽

Author(s):

Patrick Bergman ◽

Maria Hagströmer

Keyword(s):

Physical Activity ◽

Sample Size ◽

Large Sample Size ◽

Intensity Level ◽

Sample Sizes ◽

Activity Data ◽

Convenience Sample ◽

Mean Values ◽

Repeated Observations ◽

Measurement Protocol

Abstract BACKGROUND Measuring physical activity and sedentary behavior accurately remains a challenge. When describing the uncertainty of mean values or when making group comparisons, minimising Standard Error of the Mean (SEM) is important. The sample size and the number of repeated observations within each subject influence the size of the SEM. In this study we have investigated how different combinations of sample sizes and repeated observations influence the magnitude of the SEM. METHODS A convenience sample were asked to wear an accelerometer for 28 consecutive days. Based on the within and between subject variances the SEM for the different combinations of sample sizes and number of monitored days was calculated. RESULTS Fifty subjects (67% women, mean±SD age 41±19 years) were included. The analyses showed, independent of which intensity level of physical activity or how measurement protocol was designed, that the largest reductions in SEM was seen as the sample size were increased. The same magnitude in reductions to SEM was not seen for increasing the number of repeated measurement days within each subject. CONCLUSION The most effective way of reducing the SEM is to have a large sample size rather than a long observation period within each individual. Even though the importance of reducing the SEM to increase the power of detecting differences between groups is well-known it is seldom considered when developing appropriate protocols for accelerometer based research. Therefore the results presented herein serves to highlight this fact and have the potential to stimulate debate and challenge current best practice recommendations of accelerometer based physical activity research.

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Supplemental Material for The “Situative Nature” of Competence and Value Beliefs and the Predictive Power of Autonomy Support: A Multilevel Investigation of Repeated Observations

Journal of Educational Psychology ◽

10.1037/edu0000680.supp ◽

2021 ◽

Keyword(s):

Autonomy Support ◽

Predictive Power ◽

Repeated Observations

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Multifactor Modeling and Analysis of Repeated Observations of Biological Objects

Moscow University Computational Mathematics and Cybernetics ◽

10.3103/s0278641921020011 ◽

2021 ◽

Vol 45 (2) ◽

pp. 45-52

Author(s):

A. G. Belov ◽

A. E. Polienko ◽

O. A. Belova

Keyword(s):

Modeling And Analysis ◽

Biological Objects ◽

Repeated Observations

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D. Deductive, Inductive, and Retroductive Reasoning

Research Methods in the Social Sciences: An A-Z of key concepts ◽

10.1093/hepl/9780198850298.003.0019 ◽

2021 ◽

pp. 77-81

Author(s):

Dominik Giese ◽

Kai-Uwe Schnapp

Keyword(s):

Observational Studies ◽

Scientific Work ◽

Basic Structure ◽

Logical Structure ◽

Repeated Observations ◽

New Knowledge

This chapter looks at deduction, induction, and retroduction, which are three forms of reasoning that explain observations or develop new explanations from observations, by connecting sentences to a logical structure. Deduction explains individual occurrences of a phenomenon based on general sentences (laws) and respective circumstances. Induction derives general sentences (laws) from repeated observations of similar events. Retroduction, also often referred to as ‘abduction’, is an educated guess about the likely explanation for an observation, which can then be tested. The purpose of applying these forms of reasoning to observational studies is to make logic an explicit tool that applies extant knowledge, or develops new knowledge. While deduction applies extant knowledge, induction and retroduction develop new knowledge. The basic structure of all three forms of reasoning is derived from classical syllogisms (arguments), i.e. a structure in language that combines sentences (premises) to a conclusion. The chapter then considers examples of scientific work that applies the three forms of reasoning.

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Imaginary Mars

10.23943/princeton/9780691209258.003.0004 ◽

2020 ◽

pp. 46-54

Author(s):

David A. Weintraub

Keyword(s):

Nineteenth Century ◽

Physical Environment ◽

Temperate Climate ◽

Running Water ◽

Repeated Observations

This chapter cites astronomers that began imagining a Mars that was in every way like Earth and began terraforming Mars in their minds in the 1830s. It explores the act of terraforming Mars that would change its physical environment as it would become an Earth-like world where humans could live, with a temperate climate, running water, and a breathable atmosphere. It also talks about Earthbound astronomers in the nineteenth century that could not actually terraform Mars but could reshape their collective understanding of Mars and change it from a hostile world into one where humans, butterflies, and ferns could all live. The chapter emphasizes how imagination combined with herd instinct could become powerful tools for self-deception. It mentions Wolff Beer and Johann Heinrich von Mädler, who carried out the pioneering work of terraforming Mars and carried out a program of repeated observations of Mars from 1831 through 1839.

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Simultaneous inference for multiple marginal generalized estimating equation models

Statistical Methods in Medical Research ◽

10.1177/0962280219873005 ◽

2019 ◽

Vol 29 (6) ◽

pp. 1746-1762 ◽

Cited By ~ 1

Author(s):

Robin Ristl ◽

Ludwig Hothorn ◽

Christian Ritz ◽

Martin Posch

Keyword(s):

Estimating Equations ◽

Estimating Equation ◽

Small Sample ◽

Regression Coefficients ◽

Generalized Estimating Equation ◽

Type I ◽

Simultaneous Inference ◽

Multiple Endpoints ◽

Repeated Observations ◽

Generalized Estimating

Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.

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