scholarly journals The Statistical Power and Confidence of Some Key Comparison Analysis Methods to Correctly Identify Participant Bias

Metrology ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 52-73
Author(s):  
Ellie Molloy ◽  
Annette Koo ◽  
Blair D. Hall ◽  
Rebecca Harding
Keyword(s):  
Statistical Power ◽  
Synthetic Data ◽  
Data Sets ◽  
Comparison Analysis ◽  
Measurement Capability ◽  
Common Mean ◽  
Analysis Methods ◽  
The Common ◽  
Consistent Subset ◽  
International Measurement

The validity of calibration and measurement capability (CMC) claims by national metrology institutes is supported by the results of international measurement comparisons. Many methods of comparison analysis are described in the literature and some have been recommended by CIPM Consultative Committees. However, the power of various methods to correctly identify biased results is not well understood. In this work, the statistical power and confidence of some methods of interest to the CIPM Consultative Committees were assessed using synthetic data sets with known properties. Our results show that the common mean model with largest consistent subset delivers the highest statistical power under conditions likely to prevail in mature technical fields, where most participants are in agreement and CMC claims can reasonably be supported by the results of the comparison. Our approach to testing methods is easily applicable to other comparison scenarios or analysis methods and will help the metrology community to choose appropriate analysis methods for comparisons in mature technical fields.

Modelling and simulation for metabolomics data analysis

2005 ◽  
Vol 33 (6) ◽  
pp. 1427-1429 ◽  
Author(s):  
P. Mendes ◽  
D. Camacho ◽  
A. de la Fuente
Keyword(s):  
Data Analysis ◽  
Synthetic Data ◽  
Network Models ◽  
Large Data ◽  
Biochemical Network ◽  
Data Sets ◽  
Metabolomics Data ◽  
Analysis Methods ◽  
True Knowledge ◽  
Data Analysis Methods

The advent of large data sets, such as those produced in metabolomics, presents a considerable challenge in terms of their interpretation. Several mathematical and statistical methods have been proposed to analyse these data, and new ones continue to appear. However, these methods often disagree in their analyses, and their results are hard to interpret. A major contributing factor for the difficulties in interpreting these data lies in the data analysis methods themselves, which have not been thoroughly studied under controlled conditions. We have been producing synthetic data sets by simulation of realistic biochemical network models with the purpose of comparing data analysis methods. Because we have full knowledge of the underlying ‘biochemistry’ of these models, we are better able to judge how well the analyses reflect true knowledge about the system. Another advantage is that the level of noise in these data is under our control and this allows for studying how the inferences are degraded by noise. Using such a framework, we have studied the extent to which correlation analysis of metabolomics data sets is capable of recovering features of the biochemical system. We were able to identify four major metabolic regulatory configurations that result in strong metabolite correlations. This example demonstrates the utility of biochemical simulation in the analysis of metabolomics data.

scholarly journals Fast and robust common-reflection-surface parameter estimation

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. O1-O13 ◽  
Author(s):  
Anders U. Waldeland ◽  
Hao Zhao ◽  
Jorge H. Faccipieri ◽  
Anne H. Schistad Solberg ◽  
Leiv-J. Gelius
Keyword(s):  
Signal To Noise Ratio ◽  
Synthetic Data ◽  
Real Data ◽  
Structure Tensor ◽  
Data Sets ◽  
Fast Method ◽  
Surface Parameter ◽  
Common Reflection Surface ◽  
The Common ◽  
The Cost

The common-reflection-surface (CRS) method offers a stack with higher signal-to-noise ratio at the cost of a time-consuming semblance search to obtain the stacking parameters. We have developed a fast method for extracting the CRS parameters using local slope and curvature. We estimate the slope and curvature with the gradient structure tensor and quadratic structure tensor on stacked data. This is done under the assumption that a stacking velocity is already available. Our method was compared with an existing slope-based method, in which the slope is extracted from prestack data. An experiment on synthetic data shows that our method has increased robustness against noise compared with the existing method. When applied to two real data sets, our method achieves accuracy comparable with the pragmatic and full semblance searches. Our method has the advantage of being approximately two and four orders of magnitude faster than the semblance searches.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2014 ◽  
Vol 7 (3) ◽  
pp. 781-797 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Environmental Protection Agency ◽  
Synthetic Data ◽  
Analytic Solutions ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

Reverse time migration

Geophysics ◽  
1983 ◽  
Vol 48 (11) ◽  
pp. 1514-1524 ◽  
Author(s):  
Edip Baysal ◽  
Dan D. Kosloff ◽  
John W. C. Sherwood
Keyword(s):  
Wave Amplitude ◽  
Synthetic Data ◽  
Data Sets ◽  
Field Observations ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Migration Method ◽  
Zero Offset ◽  
Time Extrapolation

Migration of stacked or zero‐offset sections is based on deriving the wave amplitude in space from wave field observations at the surface. Conventionally this calculation has been carried out through a depth extrapolation. We examine the alternative of carrying out the migration through a reverse time extrapolation. This approach may offer improvements over existing migration methods, especially in cases of steeply dipping structures with strong velocity contrasts. This migration method is tested using appropriate synthetic data sets.

Quasi-2D inversion of DCR and TDEM data for shallow investigations

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F239-F250 ◽  
Author(s):  
Fernando A. Monteiro Santos ◽  
Hesham M. El-Kaliouby
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Complex Environments ◽  
Inversion Algorithm ◽  
2D Inversion ◽  
New Approach ◽  
Earth Models ◽  
Time Domain Electromagnetic ◽  
Inversion Techniques

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

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