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Libros sobre el tema "Parameter uncertainty"

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Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 31 de enero de 2023

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1

Prat, Julien. Dynamic incentive contracts under parameter uncertainty. Cambridge, MA: National Bureau of Economic Research, 2010.

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2

Frewer, Geoff. Taxation and parameter uncertainty: Some examples. Coventry: University of Warwick,Department of Economics, 1986.

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3

Edge, Rochelle Mary. Welfare-maximizing monetary policy under parameter uncertainty. San Francisco]: Federal Reserve Bank of San Francisco, 2007.

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4

Cateau, Gino. Monetary policy under model and data-parameter uncertainty. Ottawa: Bank of Canada, 2005.

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5

Froot, Kenneth. The pricing of event risks with parameter uncertainty. Cambridge, MA: National Bureau of Economic Research, 2001.

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6

Larsen, Glen A. Universal currency hedging for international equity portfolios under parameter uncertainty. Bloomington, Ind: Indiana University, School of Business, 1997.

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7

Kimura, Takeshi. Optimal monetary policy in a micro-founded model with parameter uncertainty. Washington, D.C: Federal Reserve Board, 2003.

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8

Shui wen mo xing can shu gu ji fang fa ji can shu gu ji bu que ding xing yan jiu. Zhengzhou Shi: Huang He shui li chu ban she, 2010.

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9

Giannoni, Marc Paolo. Robust optimal policy in a forward-looking model with parameter and shock uncertainty. Cambridge, Mass: National Bureau of Economic Research, 2006.

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10

Chang-Jin, Kim. Sources of monetary growth uncertainty and economic activity: The time-varying-parameter model with heteroskedasticity in the disturbance terms. [Toronto, Ont: York University, Dept. of Economics, 1990.

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11

Matasov, A. I. Estimators for uncertain dynamic systems. Dordrecht: Kluwer Academic Publishers, 1998.

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12

Ackermann, Jürgen. Robust control: Systems with uncertain physical parameters. London: Springer, 1993.

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13

Ackermann, J. Robust control: Systems with uncertain physical parameters. London: Springer-Verlag, 1993.

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14

Ackermann, J. Robust Control: Systems with Uncertain Physical Parameters. London: Springer London, 1993.

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15

Wu, Ligang, Peng Shi y Xiaojie Su. Sliding Mode Control of Uncertain Parameter-Switching Hybrid Systems. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118862612.

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16

Chakraverty, Snehashish, Rajarama Mohan Jena y Subrat Kumar Jena. Time-Fractional Order Biological Systems with Uncertain Parameters. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-02423-8.

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17

Hammitt, James K. Subjective-probability-based scenarios for uncertain input parameters: Stratospheric ozone depletion. Santa Monica, CA: Rand, 1990.

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18

Hammitt, James K. Subjective probability based scenarios for uncertain input parameters: Stratospheric ozone depletion. Santa Monica, CA: Rand, 1990.

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19

Meyer, P. D. Information on hydrologic conceptual models, parameters, uncertainty analysis, and data sources for dose assessments at decommissioning sites. Washington, DC: Division of Risk Analysis and Applications, Office of Nuclear Regulatory Reseach, U.S. Nuclear Regulatory Commission, 1999.

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20

Harvey, Campbell R., John C. Liechty y Merrill W. Liechty. Parameter Uncertainty in Asset Allocation. Oxford University Press, 2011. http://dx.doi.org/10.1093/oxfordhb/9780199553433.013.0013.

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21

Renard, Philippe, Frederick Delay, Daniel M. Tartakovsky y Velimir V. Vesselinov, eds. Parameter Estimation and Uncertainty Quantification in Water Resources Modeling. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88963-674-7.

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22

Marino, Miguel A. Subsurface Flow and Contamination: Methods of Analysis and Parameter Uncertainty. Proquest Info & Learning, 1987.

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23

Robust control design with real parameter uncertainty using absolute stability theory. [Washington, DC: National Aeronautics and Space Administration, 1993.

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24

Robust control design with real parameter uncertainty using absolute stability theory. [Washington, DC: National Aeronautics and Space Administration, 1993.

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25

Robert, Fischl y United States. National Aeronautics and Space Administration., eds. Robust control of systems with real parameter uncertainty and unmodelled dynamics: Annual progress report. [Washington, DC: National Aeronautics and Space Administration, 1991.

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26

Kirczi, S. Control of Active Suspension With Parameter Uncertainty and Non-White Road Unevenness Disturbance Input/902283. Society of Automotive Engineers, 1990.

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27

Robust control of systems with real parameter uncertainty and unmodelled dynamics: Semi-annual progress report. [Washington, DC: National Aeronautics and Space Administration, 1990.

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28

Zimmerman, D. A. Comparison of Parameter Estimation and Sensitivity Analysis Techniques and Their Impact on the Uncertainty in Ground Water Flow Model Predictions. United States Government Printing, 1991.

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29

Risk-Managing the Uncertainty in VaR Model Parameters. New York: McGraw-Hill, 2010.

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30

Hankin, David, Michael S. Mohr y Kenneth B. Newman. Sampling Theory. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198815792.001.0001.

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We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and statistically sound inferences about the population as a whole. The inferences fall into two broad categories: (a) estimation of simple descriptive population parameters, such as means, totals, or proportions, for variables of interest, and (b) estimation of uncertainty associated with estimated parameter values. Although the targets of estimation are few and simple, estimates of means, totals, or proportions see important and often controversial uses in management of natural resources and in fundamental ecological research, but few ecologists or natural resource scientists have formal training in sampling theory. We emphasize the classical design-based approach to sampling in which variable values associated with units are regarded as fixed and uncertainty of estimation arises via various randomization strategies that may be used to select samples. In addition to covering standard topics such as simple random, systematic, cluster, unequal probability (stressing the generality of Horvitz–Thompson estimation), multi-stage, and multi-phase sampling, we also consider adaptive sampling, spatially balanced sampling, and sampling through time, three areas of special importance for ecologists and natural resource scientists. The text is directed to undergraduate seniors, graduate students, and practicing professionals. Problems emphasize application of the theory and R programming in ecological and natural resource settings.
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31

M, Adelman Howard, Sobieski Jaroslaw y Langley Research Center, eds. Optimization for minimum sensitivity to uncertain parameters. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1994.

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32

Oakley, Jeremy E. y Helen E. Clough. Sensitivity analysis in microbial risk assessment: Vero-cytotoxigenic E. coli O157 in farm-pasteurized milk. Editado por Anthony O'Hagan y Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.4.

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This article discusses the use of Bayesian methods for performing uncertainty analysis in complex computer models, focusing on a mechanistic model that has been applied in a risk assessment of contamination of farm-pasteurized milk with the bacterium Vero-cytotoxigenic E. coli (VTEC) O157. The VTEC model has uncertain input parameters, which makes outputs from the model used to inform the risk assessment also uncertain. The question that arises is how to reduce output uncertainty in the most efficient manner possible. The article first provides an overview of microbial risk assessment before analysing the frequency and consequences of food-borne outbreaks associated with VTEC O157. It then introduces the risk assessment model, along with model input distributions. Finally, it presents the results of a variance-based sensitivity analysis that was conducted to identify the most important uncertain model inputs.
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33

Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
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34

Gao, Huijun y Xianwei Li. Robust Filtering for Uncertain Systems: A Parameter-Dependent Approach. Springer International Publishing AG, 2016.

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35

Gao, Huijun y Xianwei Li. Robust Filtering for Uncertain Systems: A Parameter-Dependent Approach. Springer London, Limited, 2014.

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36

Shi, Peng, Ligang Wu y Xiaojie Su. Sliding Mode Control of Uncertain Parameter-Switching Hybrid Systems. Wiley & Sons, Limited, John, 2014.

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37

Shi, Peng, Ligang Wu y Xiaojie Su. Sliding Mode Control of Uncertain Parameter-Switching Hybrid Systems. Wiley & Sons, Incorporated, John, 2014.

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38

Shi, Peng, Ligang Wu y Xiaojie Su. Sliding Mode Control of Uncertain Parameter-Switching Hybrid Systems. Wiley & Sons, Incorporated, John, 2014.

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39

Gao, Huijun y Xianwei Li. Robust Filtering for Uncertain Systems: A Parameter-Dependent Approach. Springer, 2014.

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40

Shi, Peng, Ligang Wu y Xiaojie Su. Sliding Mode Control of Uncertain Parameter-Switching Hybrid Systems. Wiley & Sons, Incorporated, John, 2014.

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41

Chakraverty, Snehashish, Subrat Kumar Jena y Rajarama Mohan Jena. Time-Fractional Order Biological Systems with Uncertain Parameters. Springer International Publishing AG, 2020.

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42

Chakraverty, Snehashish, Subrat Kumar Jena y Rajarama Mohan Jena. Time-Fractional Order Biological Systems with Uncertain Parameters. Morgan & Claypool Publishers, 2020.

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43

Chakraverty, Snehashish, Subrat Kumar Jena y Rajarama Mohan Jena. Time-Fractional Order Biological Systems with Uncertain Parameters. Morgan & Claypool Publishers, 2020.

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44

Chakraverty, Snehashish, Subrat Kumar Jena y Rajarama Mohan Jena. Time-Fractional Order Biological Systems with Uncertain Parameters. Morgan & Claypool Publishers, 2020.

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45

Robust Control of Linear Systems Subject to Uncertain Time-Varying Parameters. Springer, 2006.

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46

Amato, Francesco. Robust Control of Linear Systems Subject to Uncertain Time-Varying Parameters. Springer London, Limited, 2006.

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47

Robust Control of Linear Systems Subject to Uncertain Time-Varying Parameters. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-33276-6.

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48

Advances in Nuclear Science and Technology: Volume 14 Sensitivity and Uncertainty Analysis of Reactor Performance Parameters. Springer, 2011.

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49

Sanderson, Benjamin Mark. Uncertainty Quantification in Multi-Model Ensembles. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.707.

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Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.
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50

National Aeronautics and Space Administration (NASA) Staff. On the Calculation of Uncertainty Statistics with Error Bounds for Cfd Calculations Containing Random Parameters and Fields. Independently Published, 2019.

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