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1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

Kimura, Takeshi. Optimal monetary policy in a micro-founded model with parameter uncertainty. 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. 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. 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. York University, Dept. of Economics, 1990.

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11

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

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12

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

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13

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

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14

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

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15

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

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16

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

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17

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

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18

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

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19

Harvey, Campbell R., John C. Liechty, and 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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20

Renard, Philippe, Frederick Delay, Daniel M. Tartakovsky, and 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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21

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

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22

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

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23

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

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24

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

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25

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

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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

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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28

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

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29

Hankin, David, Michael S. Mohr, and 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) estimati
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30

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 to
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31

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

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32

Oakley, Jeremy E., and Helen E. Clough. Sensitivity analysis in microbial risk assessment: Vero-cytotoxigenic E. coli O157 in farm-pasteurized milk. Edited by Anthony O'Hagan and 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 a
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33

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

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34

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

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35

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

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36

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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37

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

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38

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 qu
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39

Deterministic and probabilistic evaluations for uncertainty in pipe fracture parameters in leak-before-break and in-service flaw evaluations. Division of Engineering Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1996.

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40

N, Ghadiali, U.S. Nuclear Regulatory Commission. Office of Nuclear Regulatory Research. Division of Engineering Technology., and Battelle Memorial Institute, eds. Deterministic and probabilistic evaluations for uncertainty in pipe fracture parameters in leak-before-break and in-service flaw evaluations. Division of Engineering Technology, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1996.

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41

United States. National Aeronautics and Space Administration., ed. Stochastic model of the NASA/MSFC ground facility for large space structures with uncertain parameters, report. Dept. of Mathematics, University of Alabama, 1988.

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42

Stochastic model of the NASA/MSFC ground facility for large space structures with uncertain parameters: Part II, the maximum entropy approach. Dept. of Mathematics, University of Alabama, 1989.

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43

William A, Schabas. Part 2 Jurisdiction, Admissibility, and Applicable Law: Compétence, Recevabilité, Et Droit Applicable, Art.7 Crimes against humanity/Crimes contre l’humanité. Oxford University Press, 2016. http://dx.doi.org/10.1093/law/9780198739777.003.0009.

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This chapter comments on Article 7 of the Rome Statute of the International Criminal Court. Article 7 defines crimes against humanity, one of four categories of offence within the subject-matter jurisdiction of the International Criminal Court. The classic definitions of crimes against humanity, in such instruments as the Charter of the Nuremberg Tribunal, are vague and open-ended, leaving courts to interpret the scope of such expressions as ‘persecution’ and ‘inhumane acts’. Out of concern with the uncertain parameters of the crime, the drafters of the Rome Statute included extra language des
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44

Salleh, Dani, and Mazlan Ismail. Infrastructure procurement framework for local authority. UUM Press, 2015. http://dx.doi.org/10.32890/9789670474434.

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The spread of infrastructure requirements and variety in mechanisms used to secure contributions (infrastructure provision) from private sector was a reflection of the institutional framework in planning system.The study has identified that although both private and local authorities have a good understanding of the fundamental of concept of local infrastructure provision and the arguments for and against the use of private provision, there are still considerable areas of uncertainty surrounding the precise definition (as prescribed in the relevant legislations) and measurements of the key ele
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45

Freitag, Lisa. Extreme Caregiving. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190491789.001.0001.

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Raising a child with multiple special needs or disabilities is a time-consuming and difficult task that exceeds the usual parameters of parenting. This book examines all the facets of that task, from the better-known physical, financial, and emotional burdens to the previously invisible moral work involved. Drawing from narratives written by parents of children with a variety of special needs, academic research in ethics and disability, and personal experience in pediatrics, this book begins to recognize the moral consequences of providing long-term care for a child with complex needs. Using a
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46

Horn, Gerd-Rainer. The Moment of Liberation in Western Europe. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780199587919.001.0001.

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The moment of liberation in Western Europe, 1943-1948, regards the final two years of World War II and the immediate post-liberation period as a moment in twentieth century history, when the shape and contours of postwar Western Europe appeared highly uncertain and various alternatives and conflicting visions were up for grabs. After close to six years of total war, Nazi terror and brutal occupation policies, a growing number of Europeans were no longer content solely to fight for national liberation from fascist control. Having staked their lives in military and civilian resistance to Nazism
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47

Busuioc, Aristita, and Alexandru Dumitrescu. Empirical-Statistical Downscaling: Nonlinear Statistical Downscaling. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.770.

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This is an advance summary of a forthcoming article in the Oxford Research Encyclopedia of Climate Science. Please check back later for the full article.The concept of statistical downscaling or empirical-statistical downscaling became a distinct and important scientific approach in climate science in recent decades, when the climate change issue and assessment of climate change impact on various social and natural systems have become international challenges. Global climate models are the best tools for estimating future climate conditions. Even if improvements can be made in state-of-the art
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