Bibliografie tematiche / Predictive uncertainty quantification

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Letteratura scientifica selezionata sul tema "Predictive uncertainty quantification"

Autore: Grafiati
Pubblicato: 5 ottobre 2024
Ultima modifica: 31 luglio 2025

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Articoli di riviste sul tema "Predictive uncertainty quantification"

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Cacuci, Dan Gabriel. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems." Energies 15, no. 17 (2022): 6379. http://dx.doi.org/10.3390/en15176379.

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The Special Issue “Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems” comprises nine articles that present important applications of concepts for performing sensitivity analyses and uncertainty quantifications of models of nuclear energy systems [...]
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Csillag, Daniel, Lucas Monteiro Paes, Thiago Ramos, et al. "AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15494–502. http://dx.doi.org/10.1609/aaai.v37i13.26837.

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Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present AmnioML, a machine learning solution that leverages deep learning and conformal prediction to output fast and accurate volume estimates and segmentation masks from fetal MRIs with Dice coefficient over 0.9. Also, we make available a novel, curated dataset for fetal MRIs with 853 exams and benchmark the performance of many recent deep learning architectures. In addition, we introduce a conforma
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Lew, Jiann-Shiun, and Jer-Nan Juang. "Robust Generalized Predictive Control with Uncertainty Quantification." Journal of Guidance, Control, and Dynamics 35, no. 3 (2012): 930–37. http://dx.doi.org/10.2514/1.54510.

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Karimi, Hamed, and Reza Samavi. "Quantifying Deep Learning Model Uncertainty in Conformal Prediction." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 142–48. http://dx.doi.org/10.1609/aaaiss.v1i1.27492.

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Precise estimation of predictive uncertainty in deep neural networks is a critical requirement for reliable decision-making in machine learning and statistical modeling, particularly in the context of medical AI. Conformal Prediction (CP) has emerged as a promising framework for representing the model uncertainty by providing well-calibrated confidence levels for individual predictions. However, the quantification of model uncertainty in conformal prediction remains an active research area, yet to be fully addressed. In this paper, we explore state-of-the-art CP methodologies and their theoret
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Serenko, I. A., Y. V. Dorn, S. R. Singh, and A. V. Kornaev. "Room for Uncertainty in Remaining Useful Life Estimation for Turbofan Jet Engines." Nelineinaya Dinamika 20, no. 5 (2024): 933–43. https://doi.org/10.20537/nd241218.

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This work addresses uncertainty quantification in machine learning, treating it as a hidden parameter of the model that estimates variance in training data, thereby enhancing the interpretability of predictive models. By predicting both the target value and the certainty of the prediction, combined with deep ensembling to study model uncertainty, the proposed method aims to increase model accuracy. The approach was applied to the well-known problem of Remaining Useful Life (RUL) estimation for turbofan jet engines using NASA’s dataset. The method demonstrated competitive results compared to ot
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Akitaya, Kento, and Masaatsu Aichi. "Land Subsidence Model Inversion with the Estimation of Both Model Parameter Uncertainty and Predictive Uncertainty Using an Evolutionary-Based Data Assimilation (EDA) and Ensemble Model Output Statistics (EMOS)." Water 16, no. 3 (2024): 423. http://dx.doi.org/10.3390/w16030423.

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The nonlinearity nature of land subsidence and limited observations cause premature convergence in typical data assimilation methods, leading to both underestimation and miscalculation of uncertainty in model parameters and prediction. This study focuses on a promising approach, the combination of evolutionary-based data assimilation (EDA) and ensemble model output statistics (EMOS), to investigate its performance in land subsidence modeling using EDA with a smoothing approach for parameter uncertainty quantification and EMOS for predictive uncertainty quantification. The methodology was teste
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Sriprasert, Soraida, and Patchanok Srisuradetchai. "Multi-K KNN regression with bootstrap aggregation: Accurate predictions and alternative prediction intervals." Edelweiss Applied Science and Technology 9, no. 5 (2025): 2750–64. https://doi.org/10.55214/25768484.v9i5.7589.

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The k-nearest neighbors (KNN) algorithm is widely recognized for its simplicity and flexibility in modeling complex, non-linear relationships; however, standard KNN regression does not inherently provide prediction intervals (PIs), presenting a persistent challenge for uncertainty quantification. This study introduces a bootstrap-based multi-K approach specifically designed to construct robust prediction intervals in KNN regression. By systematically aggregating predictions across multiple neighborhood sizes through ensemble techniques and bootstrap resampling, the method effectively quantifie
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Chala, Ayele Tesema, and Richard Ray. "Uncertainty Quantification in Shear Wave Velocity Predictions: Integrating Explainable Machine Learning and Bayesian Inference." Applied Sciences 15, no. 3 (2025): 1409. https://doi.org/10.3390/app15031409.

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The accurate prediction of shear wave velocity (Vs) is critical for earthquake engineering applications. However, the prediction is inevitably influenced by geotechnical variability and various sources of uncertainty. This paper investigates the effectiveness of integrating explainable machine learning (ML) model and Bayesian generalized linear model (GLM) to enhance both predictive accuracy and uncertainty quantification in Vs prediction. The study utilizes an Extreme Gradient Boosting (XGBoost) algorithm coupled with Shapley Additive Explanations (SHAPs) and partial dependency analysis to id
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Ayed, Safa Ben, Roozbeh Sadeghian Broujeny, and Rachid Tahar Hamza. "Remaining Useful Life Prediction with Uncertainty Quantification Using Evidential Deep Learning." Journal of Artificial Intelligence and Soft Computing Research 15, no. 1 (2024): 37–55. https://doi.org/10.2478/jaiscr-2025-0003.

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Abstract Predictive Maintenance presents an important and challenging task in Industry 4.0. It aims to prevent premature failures and reduce costs by avoiding unnecessary maintenance tasks. This involves estimating the Remaining Useful Life (RUL), which provides critical information for decision makers and planners of future maintenance activities. However, RUL prediction is not simple due to the imperfections in monitoring data, making effective Predictive Maintenance challenging. To address this issue, this article proposes an Evidential Deep Learning (EDL) based method to predict the RUL an
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Plesner, Andreas, Allan P. Engsig-Karup, and Hans True. "Detecting Railway Track Irregularities with Data-driven Uncertainty Quantification." Highlights of Vehicles 3, no. 1 (2025): 1–14. https://doi.org/10.54175/hveh3010001.

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This study addresses the critical challenge of assessing railway track irregularities using advanced machine learning techniques, specifically convolutional neural networks (CNNs) and conformal prediction. Leveraging high-fidelity sensor data from high-speed trains, we propose a novel CNN model that significantly outperforms state-of-the-art results in predicting track irregularities. Our CNN architecture, optimized through extensive hyperparameter tuning, comprises multiple convolutional layers with batch normalization, Exponential Linear Unit (ELU) activation functions, and dropout regulariz
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Tesi sul tema "Predictive uncertainty quantification"

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Lonsdale, Jack Henry. "Predictive modelling and uncertainty quantification of UK forest growth." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/16202.

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Forestry in the UK is dominated by coniferous plantations. Sitka spruce (Picea sitchensis) and Scots pine (Pinus sylvestris) are the most prevalent species and are mostly grown in single age mono-culture stands. Forest strategy for Scotland, England, and Wales all include efforts to achieve further afforestation. The aim of this afforestation is to provide a multi-functional forest with a broad range of benefits. Due to the time scale involved in forestry, accurate forecasts of stand productivity (along with clearly defined uncertainties) are essential to forest managers. These can be provided
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Gligorijevic, Djordje. "Predictive Uncertainty Quantification and Explainable Machine Learning in Healthcare." Diss., Temple University Libraries, 2018. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/520057.

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Computer and Information Science<br>Ph.D.<br>Predictive modeling is an ever-increasingly important part of decision making. The advances in Machine Learning predictive modeling have spread across many domains bringing significant improvements in performance and providing unique opportunities for novel discoveries. A notably important domains of the human world are medical and healthcare domains, which take care of peoples' wellbeing. And while being one of the most developed areas of science with active research, there are many ways they can be improved. In particular, novel tools developed ba
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Zaffran, Margaux. "Post-hoc predictive uncertainty quantification : methods with applications to electricity price forecasting." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX033.

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L'essor d'algorithmes d'apprentissage statistique offre des perspectives prometteuses pour prévoir les prix de l'électricité. Cependant, ces méthodes fournissent des prévisions ponctuelles, sans indication du degré de confiance à leur accorder. Pour garantir un déploiement sûr de ces modèles prédictifs, il est crucial de quantifier leur incertitude prédictive. Cette thèse porte sur le développement d'intervalles prédictifs pour tout algorithme de prédiction. Bien que motivées par le secteur électrique, les méthodes développées, basées sur la prédiction conforme par partition (SCP), sont généri
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Riley, Matthew E. "Quantification of Model-Form, Predictive, and Parametric Uncertainties in Simulation-Based Design." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1314895435.

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Freeman, Jacob Andrew. "Optimization Under Uncertainty and Total Predictive Uncertainty for a Tractor-Trailer Base-Drag Reduction Device." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77168.

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One key outcome of this research is the design for a 3-D tractor-trailer base-drag reduction device that predicts a 41% reduction in wind-averaged drag coefficient at 57 mph (92 km/h) and that is relatively insensitive to uncertain wind speed and direction and uncertain deflection angles due to mounting accuracy and static aeroelastic loading; the best commercial device of non-optimized design achieves a 12% reduction at 65 mph. Another important outcome is the process by which the optimized design is obtained. That process includes verification and validation of the flow solver, a less comple
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Wu, Jinlong. "Predictive Turbulence Modeling with Bayesian Inference and Physics-Informed Machine Learning." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85129.

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Reynolds-Averaged Navier-Stokes (RANS) simulations are widely used for engineering design and analysis involving turbulent flows. In RANS simulations, the Reynolds stress needs closure models and the existing models have large model-form uncertainties. Therefore, the RANS simulations are known to be unreliable in many flows of engineering relevance, including flows with three-dimensional structures, swirl, pressure gradients, or curvature. This lack of accuracy in complex flows has diminished the utility of RANS simulations as a predictive tool for engineering design, analysis, optimization, a
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Cortesi, Andrea Francesco. "Predictive numerical simulations for rebuilding freestream conditions in atmospheric entry flows." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0021/document.

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Une prédiction fidèle des écoulements hypersoniques à haute enthalpie est capitale pour les missions d'entrée atmosphérique. Cependant, la présence d'incertitudes est inévitable, sur les conditions de l'écoulement libre comme sur d'autres paramètres des modèles physico-chimiques. Pour cette raison, une quantification rigoureuse de l'effet de ces incertitudes est obligatoire pour évaluer la robustesse et la prédictivité des simulations numériques. De plus, une reconstruction correcte des paramètres incertains à partir des mesures en vol peut aider à réduire le niveau d'incertitude sur les sorti
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Erbas, Demet. "Sampling strategies for uncertainty quantification in oil recovery prediction." Thesis, Heriot-Watt University, 2007. http://hdl.handle.net/10399/70.

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Whiting, Nolan Wagner. "Assessment of Model Validation, Calibration, and Prediction Approaches in the Presence of Uncertainty." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91903.

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Model validation is the process of determining the degree to which a model is an accurate representation of the true value in the real world. The results of a model validation study can be used to either quantify the model form uncertainty or to improve/calibrate the model. However, the model validation process can become complicated if there is uncertainty in the simulation and/or experimental outcomes. These uncertainties can be in the form of aleatory uncertainties due to randomness or epistemic uncertainties due to lack of knowledge. Four different approaches are used for addressing model
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Phadnis, Akash. "Uncertainty quantification and prediction for non-autonomous linear and nonlinear systems." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85476.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 189-197).<br>The science of uncertainty quantification has gained a lot of attention over recent years. This is because models of real processes always contain some elements of uncertainty, and also because real systems can be better described using stochastic components. Stochastic models can therefore be utilized to provide a most informative prediction of possible future states of the system. In light of the m
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Libri sul tema "Predictive uncertainty quantification"

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McClarren, Ryan G. Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0.

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Eva, Boegh, and International Association of Hydrological Sciences., eds. Quantification and reduction of predictive uncertainty for sustainable water resources management: Proceedings of an international symposium [held] during IUGG2007, the XXIV General Assembly of the International Union of Geodesy and Geophysics at Perugia, Italy, July 2007. International Association of Hydrological Sciences, 2007.

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Harrington, Matthew R. Predicting and Understanding the Presence of Water through Remote Sensing, Machine Learning, and Uncertainty Quantification. [publisher not identified], 2022.

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Hemez, François, and Sez Atamturktur. Predictive Modelling: Verification, Validation and Uncertainty Quantification. Wiley & Sons, Limited, John, 2018.

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McClarren, Ryan G. Uncertainty Quantification and Predictive Computational Science: A Foundation for Physical Scientists and Engineers. Springer, 2018.

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Anderson, Mark, Francois Hemez, and Scott Doebling. Model Verification and Validation in Engineering Mechanics: Theory and Applications of Uncertainty Quantification and Predictive Accuracy. John Wiley & Sons, 2005.

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Model Verification and Validation in Engineering Mechanics: Theory and Applications of Uncertainty Quantification and Predictive Accuracy. Wiley & Sons, Limited, John, 2004.

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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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Chen, Nan. Stochastic Methods for Modeling and Predicting Complex Dynamical Systems: Uncertainty Quantification, State Estimation, and Reduced-Order Models. Springer International Publishing AG, 2023.

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Capitoli di libri sul tema "Predictive uncertainty quantification"

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Svensson, Emma, Hannah Rosa Friesacher, Adam Arany, Lewis Mervin, and Ola Engkvist. "Temporal Evaluation of Uncertainty Quantification Under Distribution Shift." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72381-0_11.

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AbstractUncertainty quantification is emerging as a critical tool in high-stakes decision-making processes, where trust in automated predictions that lack accuracy and precision can be time-consuming and costly. In drug discovery, such high-stakes decisions are based on modeling the properties of potential drug compounds on biological assays. So far, existing uncertainty quantification methods have primarily been evaluated using public datasets that lack the temporal context necessary to understand their performance over time. In this work, we address the pressing need for a comprehensive, lar
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McClarren, Ryan G. "Introduction to Uncertainty Quantification and Predictive Science." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_1.

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McClarren, Ryan G. "Gaussian Process Emulators and Surrogate Models." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_10.

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McClarren, Ryan G. "Predictive Models Informed by Simulation, Measurement, and Surrogates." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_11.

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McClarren, Ryan G. "Epistemic Uncertainties: Dealing with a Lack of Knowledge." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_12.

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McClarren, Ryan G. "Probability and Statistics Preliminaries." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_2.

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McClarren, Ryan G. "Input Parameter Distributions." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_3.

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McClarren, Ryan G. "Local Sensitivity Analysis Based on Derivative Approximations." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_4.

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McClarren, Ryan G. "Regression Approximations to Estimate Sensitivities." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_5.

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McClarren, Ryan G. "Adjoint-Based Local Sensitivity Analysis." In Uncertainty Quantification and Predictive Computational Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99525-0_6.

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Atti di convegni sul tema "Predictive uncertainty quantification"

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Mossina, Luca, Joseba Dalmau, and Léo Andéol. "Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00361.

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Park, Seong-Ho, Hong Je-Gal, and Hyun-Suk Lee. "A Novel Data-Driven Soft Sensor in Metaverse Provisioning Predictive Credibility Based on Uncertainty Quantification." In 2024 IEEE International Conference on Metaverse Computing, Networking, and Applications (MetaCom). IEEE, 2024. http://dx.doi.org/10.1109/metacom62920.2024.00053.

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Duan, Jinhao, Hao Cheng, Shiqi Wang, et al. "Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models." In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.acl-long.276.

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Dehon, Victor, Paulina Quintanilla, and Antonio Del Rio Chanona. "Probabilistic Model Predictive Control for Mineral Flotation using Gaussian Processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.122018.

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Recent advancements in machine learning and time series analysis have opened new avenues for improving predictive control in complex systems such as mineral flotation. Techniques leveraging multivariate predictive control in mineral flotation have seen significant progress in recent years. However, challenges in developing an accurate dynamic model that encapsulates both the pulp and froth phases have hindered further advancements. Now, with a readily available model containing equations that describe the physics of flotation froths, an opportunity for novel control strategies presents itself.
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Berthier, Louis, Ahmed Shokry, Eric Moulines, Guillaume Ramelet, and Sylvain Desroziers. "Knowledge Discovery in Large-Scale Batch Processes through Explainable Boosted Models and Uncertainty Quantification: Application to Rubber Mixing." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.183525.

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Rubber mixing (RM) is a vital batch process producing high-quality composites, which serve as input material for manufacturing different types of final products, such as tires. Due to its complexity, this process faces two main challenges regarding the final quality: i) lack of online measurement and ii) limited comprehension of the influence of the different factors involved in the process. While data-driven and machine learning (ML) based soft-sensing methods have been widely applied to address the first challenge, the second challenge, to the best of the author's knowledge, has not yet been
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Jiet, Moses Makuei, Prateek Verma, Aahash Kamble, and Chetan Puri. "A Review on Bayesian Methods for Uncertainty Quantification in Machine Learning Models Enhancing Predictive Accuracy and Model Interpretability." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696308.

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Dekhici, Benaissa, and Michael Short. "Data-Driven Modelling of Biogas Production Using Multi-Task Gaussian Processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.121877.

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This study introduces the novel application of a Multi-Task Gaussian Process (MTGP) model to predict biogas production and critical anaerobic digestion (AD) performance indicators (soluble COD, volatile fatty acids (VFAs)), addressing feedstock variability and dynamic process behavior. We compare the MTGP against the widely used mechanistic AM2 model to evaluate its accuracy and applicability for probabilistic modeling in AD systems. The MTGP framework leverages multi-output correlations and uncertainty quantification, trained on experimental data, achieving superior predictive performance ove
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Zhou, Hao, Yanze Zhang, and Wenhao Luo. "Safety-Critical Control with Uncertainty Quantification using Adaptive Conformal Prediction." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644391.

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Grewal, Ruben, Paolo Tonella, and Andrea Stocco. "Predicting Safety Misbehaviours in Autonomous Driving Systems Using Uncertainty Quantification." In 2024 IEEE Conference on Software Testing, Verification and Validation (ICST). IEEE, 2024. http://dx.doi.org/10.1109/icst60714.2024.00016.

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Neumeier, Marion, Sebastian Dorn, Michael Botsch, and Wolfgang Utschick. "Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00350.

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Rapporti di organizzazioni sul tema "Predictive uncertainty quantification"

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Adams, Marvin. Phenomena-based Uncertainty Quantification in Predictive Coupled- Physics Reactor Simulations. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1364745.

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Favorite, Jeffrey A., Garrett James Dean, Keith C. Bledsoe, et al. Predictive Modeling, Inverse Problems, and Uncertainty Quantification with Application to Emergency Response. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1432629.

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Lawson, Matthew, Bert J. Debusschere, Habib N. Najm, Khachik Sargsyan, and Jonathan H. Frank. Uncertainty quantification of cinematic imaging for development of predictive simulations of turbulent combustion. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/1011617.

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Ye, Ming. Computational Bayesian Framework for Quantification and Reduction of Predictive Uncertainty in Subsurface Environmental Modeling. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1491235.

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Marzouk, Youssef. Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1312896.

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Cattaneo, Matias D., Richard K. Crump, and Weining Wang. Beta-Sorted Portfolios. Federal Reserve Bank of New York, 2023. http://dx.doi.org/10.59576/sr.1068.

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Abstract (sommario):
Beta-sorted portfolios—portfolios comprised of assets with similar covariation to selected risk factors—are a popular tool in empirical finance to analyze models of (conditional) expected returns. Despite their widespread use, little is known of their statistical properties in contrast to comparable procedures such as two-pass regressions. We formally investigate the properties of beta-sorted portfolio returns by casting the procedure as a two-step nonparametric estimator with a nonparametric first step and a beta-adaptive portfolios construction. Our framework rationalizes the well-known esti
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Gonzales, Lindsey M., Thomas M. Hall, Kendra L. Van Buren, Steven R. Anton, and Francois M. Hemez. Quantification of Prediction Bounds Caused by Model Form Uncertainty. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1095195.

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Adams, Jason, Brandon Berman, Joshua Michalenko, and Rina Deka. Non-conformity Scores for High-Quality Uncertainty Quantification from Conformal Prediction. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2430248.

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Vecherin, Sergey, Stephen Ketcham, Aaron Meyer, Kyle Dunn, Jacob Desmond, and Michael Parker. Short-range near-surface seismic ensemble predictions and uncertainty quantification for layered medium. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45300.

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To make a prediction for seismic signal propagation, one needs to specify physical properties and subsurface ground structure of the site. This information is frequently unknown or estimated with significant uncertainty. This paper describes a methodology for probabilistic seismic ensemble prediction for vertically stratified soils and short ranges with no in situ site characterization. Instead of specifying viscoelastic site properties, the methodology operates with probability distribution functions of these properties taking into account analytical and empirical relationships among viscoela
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Glimm, James, Yunha Lee, Kenny Q. Ye, and David H. Sharp. Prediction Using Numerical Simulations, A Bayesian Framework for Uncertainty Quantification and its Statistical Challenge. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada417842.

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