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Relevant bibliographies by topics / Statistical Uncertainty Analysis

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Academic literature on the topic 'Statistical Uncertainty Analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Statistical Uncertainty Analysis"

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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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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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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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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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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 othe

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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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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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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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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 measurem

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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 instantaneous

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Dissertations / Theses on the topic "Statistical Uncertainty Analysis"

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Mohammed, Adnan Dawood. "A study of uncertainty in applications of statistical energy analysis." Thesis, University of Southampton, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292436.

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Lingg, Andrew James. "Statistical Methods for Image Change Detection with Uncertainty." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1357249370.

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Ellis, Amanda R. "ACCOUNTING FOR MATCHING UNCERTAINTY IN PHOTOGRAPHIC IDENTIFICATION STUDIES OF WILD ANIMALS." UKnowledge, 2018. https://uknowledge.uky.edu/statistics_etds/31.

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I consider statistical modelling of data gathered by photographic identification in mark-recapture studies and propose a new method that incorporates the inherent uncertainty of photographic identification in the estimation of abundance, survival and recruitment. A hierarchical model is proposed which accepts scores assigned to pairs of photographs by pattern recognition algorithms as data and allows for uncertainty in matching photographs based on these scores. The new models incorporate latent capture histories that are treated as unknown random variables informed by the data, contrasting pa

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Falta, Michael. "Statistical and Computational Methods to Assess Uncertainty and Risk in Accounting." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16053/.

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Informed economic decisions are made on the basis of accounting data. It is therefore crucial to have rigorous and scientific approaches for measuring, modelling and forecasting accounting numbers. Dr Falta's research was motivated by two observations. Firstly, in accounting practice, decision-making often relies on subjective quantifications and forecasts of business activities and, thus, does not account for uncertainty in a rational way. Secondly, there are some academic foundations for statistical approaches to accounting, yet none has been developed carefully enough for results to p

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Venkatesan, Sreedhar, and Hanumantha Raju Hariprasad Banglore. "Probabilistic Analysis of Brake Noise : A Hierarchical Multi-fidelity Statistical Approach." Thesis, Linköpings universitet, Mekanik och hållfasthetslära, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151009.

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Computer Aided Engineering driven analysis is gaining grounds in automotive industry. Prediction of brake noise using CAE techniques has become populardue to its overall low cost as compared to physical testing. However, the presence of several uncertain parameters which affect brake noise and also the lack of basic understanding about brake noise, makes it difficult to make reliable decisions based on CAE analysis. Therefore, the confidence level in CAE techniques has to be increased to ensure reliability and robustness in the CAE solutions which support design work. One such way to achieve r

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Herman, Joseph L. "Multiple sequence analysis in the presence of alignment uncertainty." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:88a56d9f-a96e-48e3-b8dc-a73f3efc8472.

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Sequence alignment is one of the most intensely studied problems in bioinformatics, and is an important step in a wide range of analyses. An issue that has gained much attention in recent years is the fact that downstream analyses are often highly sensitive to the specific choice of alignment. One way to address this is to jointly sample alignments along with other parameters of interest. In order to extend the range of applicability of this approach, the first chapter of this thesis introduces a probabilistic evolutionary model for protein structures on a phylogenetic tree; since protein stru

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Hanittinan, Patinya. "Statistical analysis of river discharge change in the Indochinese Peninsula using largo ensemble future climate projections." Kyoto University, 2017. http://hdl.handle.net/2433/227600.

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He, Wenbin. "Exploration and Analysis of Ensemble Datasets with Statistical and Deep Learning Models." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574695259847734.

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Patel, Jiten. "Optimal design of mesostructured materials under uncertainty." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31829.

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Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Choi, Seung-Kyum; Committee Member: Muhanna, Rafi; Committee Member: Rosen, David. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Sittichok, Ketvara. "Improving Seasonal Rainfall and Streamflow Forecasting in the Sahel Region via Better Predictor Selection, Uncertainty Quantification and Forecast Economic Value Assessment." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34229.

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The Sahel region located in Western Africa is well known for its high rainfall variability. Severe and recurring droughts have plagued the region during the last three decades of the 20th century, while heavy precipitation events (with return periods of up to 1,200 years) were reported between 2007 and 2014. Vulnerability to extreme events is partly due to the fact that people are not prepared to cope with them. It would be of great benefit to farmers if information about the magnitudes of precipitation and streamflow in the upcoming rainy season were available a few months before; they cou

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Books on the topic "Statistical Uncertainty Analysis"

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1931-, DeGroot Morris H., ed. Bayesian analysis and uncertainty in economic theory. Rowman & Littlefield, 1987.

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Cyert, Richard Michael. Bayesian analysis and uncertainty in economic theory. Chapman and Hall, 1987.

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Pointe, P. R. La, and Y. Zee Ma. Uncertainty analysis and reservoir modeling. American Association of Petroleum Geologists, 2011.

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Measuring statistical evidence using relative belief. CRC Press, Taylor & Francis Group, 2015.

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Chakraverty, Snehashish. Mathematics of uncertainty modeling in the analysis of engineering and science problems. Information Science Reference, 2014.

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Stumpf, M. P. H. Handbook of statistical systems biology. John Wiley & Sons, 2011.

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Sikorska, Anna Elżbieta. Uncertainty analysis of rainfall-runoff predictions for a small urbanized basin. Komitet Gospodarki Wodnej Polskiej Akademii Nauk, 2014.

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Ayyub, Bilal M. Uncertainty analysis in engineering and sciences: Fuzzy logic, statistics, and neural network approach. Kluwer Academic Publishers, 1997.

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Fienen, Michael N. Using prediction uncertainty analysis to design hydrologic monitoring networks: Example applications from the Great Lakes Water Availability Pilot Project. U.S. Department of the Interior, U.S. Geological Survey, 2010.

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Huyse, Luc. Free-form airfoil shape optimization under uncertainty using maximum expected value and second-order second-moment strategies. ICASE, NASA Langley Research Center, 2001.

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Book chapters on the topic "Statistical Uncertainty Analysis"

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Shah, Millie, Zeinab Chitforoushzadeh, and Kevin A. Janes. "Statistical Data Analysis and Modeling." In Uncertainty in Biology. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21296-8_6.

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Little, Roderick J. A., and Donald B. Rubin. "Estimation of Imputation Uncertainty." In Statistical Analysis with Missing Data. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781119013563.ch5.

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Ricci, Paolo F. "Uncertainty: Probabilistic and Statistical Aspects." In Analysis of Catastrophes and Their Public Health Consequences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48066-0_5.

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Serapinas, Petras. "Uncertainty — statistical approach, 1/f noise and chaos." In Measurement Uncertainty in Chemical Analysis. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-05173-3_11.

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Berk, Richard A., and Jan de Leeuw. "MULTILEVEL STATISTICAL MODELS AND ECOLOGICAL SCALING." In SCALING AND UNCERTAINTY ANALYSIS IN ECOLOGY. Springer Netherlands, 2006. http://dx.doi.org/10.1007/1-4020-4663-4_4.

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Maity, Rajib. "Frequency Analysis, Risk, and Uncertainty in Hydroclimatic Analysis." In Statistical Methods in Hydrology and Hydroclimatology. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8779-0_5.

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Bevia, Vicent, Juan-Carlos Cortés, Ana Navarro-Quiles, and Jose-Vicente Romero. "Statistical Analysis of Biological Models with Uncertainty." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-53669-5_23.

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Deldossi, Laura, and Diego Zappa. "Measurement Errors and Uncertainty: A Statistical Perspective." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11363-5_17.

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Denœux, Thierry. "Statistical Inference from Ill-known Data Using Belief Functions." In Uncertainty Analysis in Econometrics with Applications. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35443-4_3.

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Conti, Pier Luigi, Daniela Marella, and Mauro Scanu. "An overview on uncertainty and estimation in statistical matching." In Analysis of Integrated Data. Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315120416-4.

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Conference papers on the topic "Statistical Uncertainty Analysis"

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Haas, A., P. Thore, and J. Arnaud. "Structural Uncertainty Determination by Statistical Analysis." In EAEG/SEG Summer Workshop - Construction of 3-D Macro Velocity-Depth Models. European Association of Geoscientists & Engineers, 1994. http://dx.doi.org/10.3997/2214-4609.201407576.

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Brown, Jeffrey. "Optimally Reducing Statistical Uncertainty Through Probabilistic Sensitivity Analysis." In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5533.

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Hartini, Entin, Dinan Andiwijayakusuma, Mike Susmikanti, A. W. Nursinta, Zaki Su’ud, and A. Waris. "Statistical Uncertainty Analysis Applied to Criticality Calculation." In THE 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN NUCLEAR SCIENCE AND ENGINEERING 2009-ICANSE 2009. AIP, 2010. http://dx.doi.org/10.1063/1.3462758.

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Glaeser, Horst G. "Uncertainty Analysis Becoming Common Practice in Safety Assessment." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31179.

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During the recent years an increasing interest in computational reactor safety analysis is to replace the conservative evaluation model calculations by best estimate calculations supplemented by uncertainty analysis of the code results. The evaluation of the margin to acceptance criteria, e.g. the maximum fuel rod clad temperature, should be based on the upper limit of the calculated uncertainty range. For example, due to power increase, licensing limits are approached. Therefore, regulators are looking closer on the way, how calculations are performed to meet these acceptance criteria. Method

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ÇOBAN, VEYSEL, and TANKUT ATAN. "STATISTICAL ANALYSIS OF BUS TRANSPORTATION NETWORKS OF ISTANBUL." In Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016). WORLD SCIENTIFIC, 2016. http://dx.doi.org/10.1142/9789813146976_0173.

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Pita, G. L., R. Francis, Z. Liu, J. Mitrani-Reiser, S. Guikema, and J. P. Pinelli. "Statistical Tools for Populating/Predicting Input Data of Risk Analysis Models." In First International Symposium on Uncertainty Modeling and Analysis and Management (ICVRAM 2011); and Fifth International Symposium on Uncertainty Modeling and Anaylsis (ISUMA). American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41170(400)57.

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Hryniewicz, Olgierd. "Statistical Decisions with Random Fuzzy Data-A Possibilistic Approach." In First International Symposium on Uncertainty Modeling and Analysis and Management (ICVRAM 2011); and Fifth International Symposium on Uncertainty Modeling and Anaylsis (ISUMA). American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41170(400)3.

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Sriver, Todd, and James Chrissis. "Framework for Mixed-Variable Optimization Under Uncertainty Using Surrogates and Statistical Selection." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4591.

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Diao, Kun, Lijun Zhang, and Dejian Meng. "Study on Statistical Analysis of Uncertainty of Disc Brake Squeal." In SAE 2014 World Congress & Exhibition. SAE International, 2014. http://dx.doi.org/10.4271/2014-01-0030.

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Xie, Wei, Barry L. Nelson, and Russell R. Barton. "Statistical uncertainty analysis for stochastic simulation with dependent input models." In 2014 Winter Simulation Conference - (WSC 2014). IEEE, 2014. http://dx.doi.org/10.1109/wsc.2014.7019931.

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Reports on the topic "Statistical Uncertainty Analysis"

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Shine, E. P. Analysis of sampling plan options for tank 16H from the perspective of statistical uncertainty. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1067371.

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Wilson, G. E. Statistically based uncertainty analysis for ranking of component importance in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10139074.

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Wilson, G. E. Statistically based uncertainty analysis for ranking of component importance in the thermal-hydraulic safety analysis of the Advanced Neutron Source Reactor. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/5485664.

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Vargas-Herrera, Hernando, Juan Jose Ospina-Tejeiro, Carlos Alfonso Huertas-Campos, et al. Monetary Policy Report - April de 2021. Banco de la República de Colombia, 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr2-2021.

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1.1 Macroeconomic summary Economic recovery has consistently outperformed the technical staff’s expectations following a steep decline in activity in the second quarter of 2020. At the same time, total and core inflation rates have fallen and remain at low levels, suggesting that a significant element of the reactivation of Colombia’s economy has been related to recovery in potential GDP. This would support the technical staff’s diagnosis of weak aggregate demand and ample excess capacity. The most recently available data on 2020 growth suggests a contraction in economic activity of 6.8%, lowe

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