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Journal articles on the topic 'Statistical Uncertainty Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

Khan, Mohammad Sajjad, Paulin Coulibaly, and Yonas Dibike. "Uncertainty analysis of statistical downscaling methods." Journal of Hydrology 319, no. 1-4 (2006): 357–82. http://dx.doi.org/10.1016/j.jhydrol.2005.06.035.

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2

Modarres, M., and T. Cadman. "Statistical uncertainty analysis in reactor risk estimation." Nuclear Engineering and Design 85, no. 3 (1985): 385–99. http://dx.doi.org/10.1016/0029-5493(85)90237-7.

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3

Nien Fan Zhang, N. Sedransk, and D. G. Jarrett. "Statistical uncertainty analysis of key comparison ccem-k2." IEEE Transactions on Instrumentation and Measurement 52, no. 2 (2003): 491–94. http://dx.doi.org/10.1109/tim.2003.811669.

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4

MACIAN, Rafael, Martin A. ZIMMERMANN, and Rakesh CHAWLA. "Statistical Uncertainty Analysis Applied to Fuel Depletion Calculations." Journal of Nuclear Science and Technology 44, no. 6 (2007): 875–85. http://dx.doi.org/10.1080/18811248.2007.9711325.

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5

Khosravi, Faramarz, Malte Müller, Michael Glaß, and Jürgen Teich. "Simulation-based uncertainty correlation modeling in reliability analysis." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 232, no. 6 (2018): 725–37. http://dx.doi.org/10.1177/1748006x18758720.

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Due to destructive effects like temperature and radiation, today’s embedded systems have to deal with unreliable components. The intensity of these effects depends on uncertain aspects like environmental or usage conditions such that highly safety-critical systems are pessimistically designed for worst-case mission profiles. These uncertain aspects may affect several components simultaneously, implying correlation across uncertainties in their reliability. This paper enables a state-of-the-art uncertainty-aware reliability analysis technique to consider multiple arbitrary correlations; in other words, components’ reliability is affected by several uncertain aspects to different degrees. This analysis technique combines reliability models such as binary decision diagrams with a Monte Carlo simulation, and derives the uncertainty distribution of the system’s reliability with insights on the mean, quantile intervals, and so on. The proposed correlation method aims at generating correlated samples from the uncertainty distribution of components’ reliability such that the shape and statistical properties of each individual distribution remain unchanged. Experimental results confirm that the proposed correlation model enables the employed uncertainty-aware analysis to accurately calculate uncertainty at system level.

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6

Guo Yu, Wei Dong, Zhuo Feng, and Peng Li. "Statistical Static Timing Analysis Considering Process Variation Model Uncertainty." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 27, no. 10 (2008): 1880–90. http://dx.doi.org/10.1109/tcad.2008.2003302.

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7

Xu, Yue-Ping, Martijn J. Booij, and Yang-Bin Tong. "Uncertainty analysis in statistical modeling of extreme hydrological events." Stochastic Environmental Research and Risk Assessment 24, no. 5 (2009): 567–78. http://dx.doi.org/10.1007/s00477-009-0337-8.

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8

Conti, Pier Luigi, Daniela Marella, and Mauro Scanu. "Uncertainty analysis for statistical matching of ordered categorical variables." Computational Statistics & Data Analysis 68 (December 2013): 311–25. http://dx.doi.org/10.1016/j.csda.2013.07.004.

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9

Batko, Wojciech, and Bartosz Przysucha. "Statistical Analysis of the Equivalent Noise Level." Archives of Acoustics 39, no. 2 (2015): 195–98. http://dx.doi.org/10.2478/aoa-2014-0023.

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Abstract The authors focus their attention on the analysis of the probability density function of the equivalent noise level, in the context of a determination of the uncertainty of the obtained results in regard to the control of environmental acoustic hazards. In so doing, they discuss problems of correctness in the applicability of the classical normal distribution for the estimation of the expected interval value of the equivalent sound level. The authors also provide a set of procedures with respect to its derivation, based upon an assumption of the determined distribution of the measurement results. The obtained results then create the plane for the correct uncertainty calculation of the results of the determined controlled environmental acoustic hazard coefficient.

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10

Sarri, A., S. Guillas, and F. Dias. "Statistical emulation of a tsunami model for sensitivity analysis and uncertainty quantification." Natural Hazards and Earth System Sciences 12, no. 6 (2012): 2003–18. http://dx.doi.org/10.5194/nhess-12-2003-2012.

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Abstract. Due to the catastrophic consequences of tsunamis, early warnings need to be issued quickly in order to mitigate the hazard. Additionally, there is a need to represent the uncertainty in the predictions of tsunami characteristics corresponding to the uncertain trigger features (e.g. either position, shape and speed of a landslide, or sea floor deformation associated with an earthquake). Unfortunately, computer models are expensive to run. This leads to significant delays in predictions and makes the uncertainty quantification impractical. Statistical emulators run almost instantaneously and may represent well the outputs of the computer model. In this paper, we use the outer product emulator to build a fast statistical surrogate of a landslide-generated tsunami computer model. This Bayesian framework enables us to build the emulator by combining prior knowledge of the computer model properties with a few carefully chosen model evaluations. The good performance of the emulator is validated using the leave-one-out method.

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11

Lehmann, R., P. von der Gathen, M. Rex, and M. Streibel. "Statistical analysis of the precision of the Match method." Atmospheric Chemistry and Physics Discussions 5, no. 3 (2005): 3225–68. http://dx.doi.org/10.5194/acpd-5-3225-2005.

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Abstract. The Match method quantifies chemical ozone loss in the polar stratosphere. The basic idea consists in calculating the forward trajectory of an air parcel that has been probed by an ozone measurement (e.g., by an ozone sonde or satellite) and finding a second ozone measurement close to this trajectory. Such an event is called a ''match''. A rate of chemical ozone destruction can be obtained by a statistical analysis of several tens of such match events. Information on the uncertainty of the calculated rate can be inferred from the scatter of the ozone mixing ratio difference (second measurement minus first measurement) associated with individual matches. A standard analysis would assume that the errors of these differences are statistically independent. However, this assumption may be violated because different matches can share a common ozone measurement, so that the errors associated with these match events become statistically dependent. Taking this effect into account, we present an analysis of the uncertainty of the final Match result. It has been applied to Match data from the Arctic winters 1995, 1996, 2000, and 2003. For these ozone-sonde Match studies the effect of the error correlation on the uncertainty estimates is rather small: compared to a standard error analysis, the uncertainty estimates increase by 15% on average. However, the effect is more pronounced for typical satellite Match analyses: for an Antarctic satellite Match study (2003), the uncertainty estimates increase by 60% on average.

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12

Rust, Philip F. "Statistical Methods of Analysis." Journal of the American Statistical Association 100, no. 471 (2005): 1094–95. http://dx.doi.org/10.1198/jasa.2005.s37.

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13

Arsham, H., and F. J. Wall. "Statistical Data Analysis Handbook." Applied Statistics 37, no. 3 (1988): 452. http://dx.doi.org/10.2307/2347325.

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14

Lim, Yong Kwan, Oh Joo Kweon, Mi-Kyung Lee, and Hye Ryoun Kim. "Assessing the measurement uncertainty of qualitative analysis in the clinical laboratory." Journal of Laboratory Medicine 44, no. 1 (2020): 3–10. http://dx.doi.org/10.1515/labmed-2019-0155.

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Abstract Measurement uncertainty is a parameter that is associated with the dispersion of measurements. Assessment of the measurement uncertainty is recommended in qualitative analyses in clinical laboratories; however, the measurement uncertainty of qualitative tests has been neglected despite the introduction of many adequate methods. We herein provide an overview of three reasonable statistical methods for quantifying the measurement uncertainties of qualitative assays, namely Bayes’ theorem, the normal distribution method, and the information theoretic approach. Unlike in quantitative analysis, the measurement uncertainty of qualitative analysis is expressed using a conditional probability, likelihood ratio, and entropy. With the necessary theoretical background, the practical applications for clinical laboratories are also provided using statistical calculations. Using statistical approaches, we hope that our review will contribute to the use of measurement uncertainty in qualitative analyses in the clinical laboratory environment.

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15

Koneshloo, Amirhossein, Dongping Du, and Yuncheng Du. "An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis." Bioengineering 7, no. 2 (2020): 62. http://dx.doi.org/10.3390/bioengineering7020062.

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Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.

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16

Lehmann, R., P. von der Gathen, M. Rex, and M. Streibel. "Statistical analysis of the precision of the Match method." Atmospheric Chemistry and Physics 5, no. 10 (2005): 2713–27. http://dx.doi.org/10.5194/acp-5-2713-2005.

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Abstract. The Match method quantifies chemical ozone loss in the polar stratosphere. The basic idea consists in calculating the forward trajectory of an air parcel that has been probed by an ozone measurement (e.g., by an ozonesonde or satellite instrument) and finding a second ozone measurement close to this trajectory. Such an event is called a "match". A rate of chemical ozone destruction can be obtained by a statistical analysis of several tens of such match events. Information on the uncertainty of the calculated rate can be inferred from the scatter of the ozone mixing ratio difference (second measurement minus first measurement) associated with individual matches. A standard analysis would assume that the errors of these differences are statistically independent. However, this assumption may be violated because different matches can share a common ozone measurement, so that the errors associated with these match events become statistically dependent. Taking this effect into account, we present an analysis of the uncertainty of the final Match result. It has been applied to Match data from the Arctic winters 1995, 1996, 2000, and 2003. For these ozonesonde Match studies the effect of the error correlation on the uncertainty estimates is rather small: compared to a standard error analysis, the uncertainty estimates increase by 15% on average. However, the effect may be more pronounced for typical satellite Match analyses: for an Antarctic satellite Match study (2003), the uncertainty estimates increase by 60% on average. The analysis showed that the random errors of the ozone measurements and the "net match errors", which result from a displacement of the second ozone measurement of a match from the required position, are of similar magnitude. This demonstrates that the criteria for accepting a match (maximum trajectory duration, match radius, spread of trajectory clusters etc.) ensure that, given the unavoidable ozone-measurement errors, the magnitude of the net match errors is adequate. The estimate of the random errors of the ozonesonde measurements agrees well with laboratory results.

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17

Prošek, Andrej, Boštjan Končar, and Matjaž Leskovar. "Uncertainty analysis of CFD benchmark case using optimal statistical estimator." Nuclear Engineering and Design 321 (September 2017): 132–43. http://dx.doi.org/10.1016/j.nucengdes.2016.12.008.

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18

Camussi, R., C. Baudet, R. Benzi, and S. Ciliberto. "Statistical uncertainty in the analysis of structure functions in turbulence." Physical Review E 54, no. 4 (1996): R3098—R3101. http://dx.doi.org/10.1103/physreve.54.r3098.

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19

Wang, Z. H., C. Jiang, X. X. Ruan, et al. "Uncertainty Propagation Analysis of T/R Modules." International Journal of Computational Methods 16, no. 07 (2019): 1850105. http://dx.doi.org/10.1142/s0219876218501050.

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This paper develops an uncertainty propagation analysis method to analyze transmit/receive (T/R) modules with uncertain parameters, such as variability and tolerances in the physical parameters and geometry produced in the manufacturing processes. The method is a combination of the variance decomposition-based sensitivity analysis and the moment-based arbitrary polynomial chaos (MBaPC). First, the electromagnetic simulation model of a practical T/R module is created. Secondly, based on the model, the sensitivity analysis is carried out to determine the sensitive parameters to the amplitude difference and the phase difference between the input and output electromagnetic signal. Thirdly, their four order statistical moments are calculated using the MBaPC. At last, according to the maximum entropy principle, the statistical moments are used to fit the probability distribution functions of the amplitude difference and the phase difference of the T/R module. The results computed by MBaPC have been validated accurate and efficient compared with Monte Carlo simulation approach.

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20

Elisavet, Amanatidou, Trikoilidou Eleni, Tsikritzis Lazaros, and Katsiouli Foteini. "Uncertainty in spectrophotometric analysis—“Error propagation break up”, a novel statistical method for uncertainty management." Talanta 85, no. 5 (2011): 2385–90. http://dx.doi.org/10.1016/j.talanta.2011.07.084.

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21

Thiene, Marco, Zahra Sharif Khodaei, and M. H. Aliabadi. "Uncertainty Analysis of Active SHM System." Key Engineering Materials 665 (September 2015): 249–52. http://dx.doi.org/10.4028/www.scientific.net/kem.665.249.

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Structural Health Monitoring (SHM) techniques have gained an increased interest to be utilised alongside NDI techniques for aircraft maintenance. However, to take the SHM methodologies from the laboratory conditions to actual structures under real load conditions requires them to be assessed in terms of reliability and robustness. In this work, a statistical analysis is carried out for an SHM system for damage detection and characterisation in composite structures. The sensitivity of the platform to parameters such as noise, sensor failure, placement tolerances and bonding has been investigated and reported.

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22

Le Fevre, Andrea. "Statistical Power Analysis." Journal of the Royal Statistical Society: Series A (Statistics in Society) 168, no. 2 (2005): 465. http://dx.doi.org/10.1111/j.1467-985x.2005.358_14.x.

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23

Cicirello, Alice, and Robin S. Langley. "Efficient parametric uncertainty analysis within the hybrid Finite Element/Statistical Energy Analysis method." Journal of Sound and Vibration 333, no. 6 (2014): 1698–717. http://dx.doi.org/10.1016/j.jsv.2013.10.040.

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24

Lang, Joseph B., and Christopher J. Lloyd. "Statistical Analysis of Categorical Data." Journal of the American Statistical Association 95, no. 450 (2000): 680. http://dx.doi.org/10.2307/2669421.

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25

Lund, Robert B., Hans von Storch, and Francis W. Zwiers. "Statistical Analysis in Climate Research." Journal of the American Statistical Association 95, no. 452 (2000): 1375. http://dx.doi.org/10.2307/2669798.

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26

Cliff, Andrew D., Daniel A. Griffith, and Carl G. Amrhein. "Multivariate Statistical Analysis for Geographers." Journal of the American Statistical Association 94, no. 446 (1999): 654. http://dx.doi.org/10.2307/2670195.

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27

Wei, William W. S., and Robert H. Shumway. "Applied Statistical Time Series Analysis." Journal of the American Statistical Association 85, no. 409 (1990): 258. http://dx.doi.org/10.2307/2289560.

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28

Moore, Thomas L., T. W. Anderson, Stanley L. Sclove, et al. "The Statistical Analysis of Data." Journal of the American Statistical Association 84, no. 407 (1989): 834. http://dx.doi.org/10.2307/2289676.

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29

Skalski, John R., N. I. Fisher, T. Lewis, and B. J. Embleton. "Statistical Analysis of Spherical Data." Journal of the American Statistical Association 84, no. 407 (1989): 850. http://dx.doi.org/10.2307/2289702.

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30

Lahiff, Maureen, Roderick J. A. Little, and Donald B. Rubin. "Statistical Analysis With Missing Data." Journal of the American Statistical Association 84, no. 405 (1989): 332. http://dx.doi.org/10.2307/2289883.

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31

Ames, Michael H., M. J. Crowder, A. C. Kimber, R. L. Smith, and T. J. Sweeting. "Statistical Analysis of Reliability Data." Journal of the American Statistical Association 87, no. 418 (1992): 589. http://dx.doi.org/10.2307/2290306.

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32

Woolson, Robert F., and Steve Selvin. "Statistical Analysis of Epidemiologic Data." Journal of the American Statistical Association 88, no. 421 (1993): 382. http://dx.doi.org/10.2307/2290745.

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33

AA, Robert D. Rutherford, and Minja Kim Choe. "Statistical Models for Causal Analysis." Journal of the American Statistical Association 89, no. 427 (1994): 1150. http://dx.doi.org/10.2307/2290974.

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34

Lange, Nicholas, and Nicholas I. Fisher. "Statistical Analysis of Circular Data." Journal of the American Statistical Association 90, no. 430 (1995): 801. http://dx.doi.org/10.2307/2291098.

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35

RJ, M. J. Crowder, A. C. Kimber, R. L. Smith, and T. J. Sweeting. "Statistical Analysis of Reliability Data." Journal of the American Statistical Association 91, no. 433 (1996): 440. http://dx.doi.org/10.2307/2291436.

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36

Chakraborti, Subha, J. V. Deshpande, A. P. Gore, and A. Shanubhogue. "Statistical Analysis of Nonnormal Data." Journal of the American Statistical Association 91, no. 436 (1996): 1750. http://dx.doi.org/10.2307/2291606.

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37

Girko, V. L. "Introduction to General Statistical Analysis." Theory of Probability & Its Applications 32, no. 2 (1988): 229–42. http://dx.doi.org/10.1137/1132033.

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38

Besag, Julian. "Statistical analysis of dirty pictures*." Journal of Applied Statistics 20, no. 5-6 (1993): 63–87. http://dx.doi.org/10.1080/02664769300000059.

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39

Resnick, Sidney, and Martin Jacobsen. "Statistical Analysis of Counting Processes." Journal of the American Statistical Association 80, no. 390 (1985): 489. http://dx.doi.org/10.2307/2287937.

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40

Greenhouse, Samuel W., and Heinz Kres. "Statistical Tables for Multivariate Analysis." Journal of the American Statistical Association 80, no. 391 (1985): 780. http://dx.doi.org/10.2307/2288513.

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41

Rosenbaum, Paul R., Larry Hedges, and Ingram Olkin. "Statistical Methods for Meta-Analysis." Journal of the American Statistical Association 82, no. 397 (1987): 350. http://dx.doi.org/10.2307/2289186.

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42

Mellor, Dale, A. V. Ivanov, and N. N. Leonenko. "Statistical Analysis of Random Fields." Applied Statistics 40, no. 1 (1991): 181. http://dx.doi.org/10.2307/2347922.

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43

Han, Ying, and Partha Lahiri. "Statistical Analysis with Linked Data." International Statistical Review 87, S1 (2018): S139—S157. http://dx.doi.org/10.1111/insr.12295.

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44

Pokern, Yvo, Benjamin Eltzner, Stephan F. Huckemann, et al. "Statistical analysis of ENDOR spectra." Proceedings of the National Academy of Sciences 118, no. 27 (2021): e2023615118. http://dx.doi.org/10.1073/pnas.2023615118.

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Electron–nuclear double resonance (ENDOR) measures the hyperfine interaction of magnetic nuclei with paramagnetic centers and is hence a powerful tool for spectroscopic investigations extending from biophysics to material science. Progress in microwave technology and the recent availability of commercial electron paramagnetic resonance (EPR) spectrometers up to an electron Larmor frequency of 263 GHz now open the opportunity for a more quantitative spectral analysis. Using representative spectra of a prototype amino acid radical in a biologically relevant enzyme, the Y122• in Escherichia coli ribonucleotide reductase, we developed a statistical model for ENDOR data and conducted statistical inference on the spectra including uncertainty estimation and hypothesis testing. Our approach in conjunction with 1H/2H isotopic labeling of Y122• in the protein unambiguously established new unexpected spectral contributions. Density functional theory (DFT) calculations and ENDOR spectral simulations indicated that these features result from the beta-methylene hyperfine coupling and are caused by a distribution of molecular conformations, likely important for the biological function of this essential radical. The results demonstrate that model-based statistical analysis in combination with state-of-the-art spectroscopy accesses information hitherto beyond standard approaches.

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45

Khodaygan, S., and M. Hafezipour. "Error Reduction in Spatial Robots Based on the Statistical Uncertainty Analysis." SAE International Journal of Materials and Manufacturing 8, no. 2 (2015): 263–70. http://dx.doi.org/10.4271/2015-01-0435.

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46

Jawadi, Fredj, Nabila Jawadi, and Abdoukarim Idi Cheffou. "A statistical analysis of uncertainty for conventional and ethical stock indexes." Quarterly Review of Economics and Finance 74 (November 2019): 9–17. http://dx.doi.org/10.1016/j.qref.2018.03.002.

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47

Zhang, Yu, Vijay P. Singh, and Aaron R. Byrd. "Basin-Scale Statistical Method for Probable Maximum Precipitation with Uncertainty Analysis." Journal of Hydrologic Engineering 24, no. 2 (2019): 04018067. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001759.

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48

Williams, M. L., G. Ilas, M. A. Jessee, et al. "A Statistical Sampling Method for Uncertainty Analysis with SCALE and XSUSA." Nuclear Technology 183, no. 3 (2013): 515–26. http://dx.doi.org/10.13182/nt12-112.

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49

Briggs, Andrew. "Probabilistic Analysis of Cost-Effectiveness Models: Statistical Representation of Parameter Uncertainty." Value in Health 8, no. 1 (2005): 1–2. http://dx.doi.org/10.1111/j.1524-4733.2005.08101.x.

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50

Sarachi, Sepideh, Kuo-lin Hsu, and Soroosh Sorooshian. "A Statistical Model for the Uncertainty Analysis of Satellite Precipitation Products." Journal of Hydrometeorology 16, no. 5 (2015): 2101–17. http://dx.doi.org/10.1175/jhm-d-15-0028.1.

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Abstract Earth-observing satellites provide a method to measure precipitation from space with good spatial and temporal coverage, but these estimates have a high degree of uncertainty associated with them. Understanding and quantifying the uncertainty of the satellite estimates can be very beneficial when using these precipitation products in hydrological applications. In this study, the generalized normal distribution (GND) model is used to model the uncertainty of the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) precipitation product. The stage IV Multisensor Precipitation Estimator (radar-based product) was used as the reference measurement. The distribution parameters of the GND model are further extended across various rainfall rates and spatial and temporal resolutions. The GND model is calibrated for an area of 5° × 5° over the southeastern United States for both summer and winter seasons from 2004 to 2009. The GND model is used to represent the joint probability distribution of satellite (PERSIANN) and radar (stage IV) rainfall. The method is further investigated for the period of 2006–08 over the Illinois watershed south of Siloam Springs, Arkansas. Results show that, using the proposed method, the estimation of the precipitation is improved in terms of percent bias and root-mean-square error.

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