Relevant bibliographies by topics / Predicting model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predicting model'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Predicting model"

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Siek, M., and D. P. Solomatine. "Nonlinear chaotic model for predicting storm surges." Nonlinear Processes in Geophysics 17, no. 5 (2010): 405–20. http://dx.doi.org/10.5194/npg-17-405-2010.

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Abstract. This paper addresses the use of the methods of nonlinear dynamics and chaos theory for building a predictive chaotic model from time series. The chaotic model predictions are made by the adaptive local models based on the dynamical neighbors found in the reconstructed phase space of the observables. We implemented the univariate and multivariate chaotic models with direct and multi-steps prediction techniques and optimized these models using an exhaustive search method. The built models were tested for predicting storm surge dynamics for different stormy conditions in the North Sea,
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Yang, Ke. "Predicting Student Performance Using Artificial Neural Networks." Journal of Arts, Society, and Education Studies 6, no. 1 (2024): 45–77. http://dx.doi.org/10.69610/j.ases.20240515.

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<p class="MsoNormal" style="text-align: justify;"><span style="font-family: Times New Roman;">This paper explores machine learning approaches to predicting student performance using artificial neural networks. By employing educational data mining and predictive modeling techniques, accurate predictions of student outcomes were achieved. The results indicate that artificial neural networks exhibit high accuracy and reliability in forecasting student academic performance. Through comprehensive analysis and empirical testing, this approach significantly enhances the effectiveness of s
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Carlsson, Leo S., Mikael Vejdemo-Johansson, Gunnar Carlsson, and Pär G. Jönsson. "Fibers of Failure: Classifying Errors in Predictive Processes." Algorithms 13, no. 6 (2020): 150. http://dx.doi.org/10.3390/a13060150.

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Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FiFa), a method to classify failure modes of predictive processes. Our method uses Mapper, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity
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Azhagusundari, Dr B., Dr John Grasias S, G. Sudha,, G. Kanimozhi,, and Dr Radhika. "Linear Regression Model in Student Prediction System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42438.

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Logistic Regression is a widely used statistical method for predicting a categorical dependent variable based on a set of independent variables. Recognized for its adaptability and frequent application, logistic regression is particularly effective in modeling binary and multinomial outcomes. This paper provides a clear and detailed exploration of the fundamental concepts of logistic regression and demonstrates its application in predictive analysis using student data. Through this practical example, the paper highlights the method's utility in identifying relationships and making informed pre
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N, Dhivya, and Shalini M. "Predicting Stroke Risk: An Effective Stroke Prediction Model Based On Neural Networks." International Journal of Research Publication and Reviews 6, no. 6 (2025): 2433–37. https://doi.org/10.55248/gengpi.6.0625.2055.

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Zhou, Nuo-Ya, and Bing Hu. "Preoperative gastric retention in endoscopic retrograde cholangiopancreatography patients: Assessing risks and optimizing outcomes." World Journal of Gastrointestinal Surgery 16, no. 12 (2024): 3655–57. http://dx.doi.org/10.4240/wjgs.v16.i12.3655.

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This article is a comment on the article by Jia et al , aiming at establishing a predictive model to predict the occurrence of preoperative gastric retention in endoscopic retrograde cholangiopancreatography preparation. We share our perspectives on this predictive model. First, further differentiation in predicting the severity of gastric retention could enhance clinical outcomes. Second, we ponder whether this predictive model can be generalized to predictions of gastric retention before various endoscopic procedures. Third, large datasets and prospective clinical validation are needed to im
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Watson-Daniels, Jamelle, David C. Parkes, and Berk Ustun. "Predictive Multiplicity in Probabilistic Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10306–14. http://dx.doi.org/10.1609/aaai.v37i9.26227.

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Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or predicting a person's risk to appear in court. Given multiple models that perform almost equally well for a prediction task, to what extent do predictions vary across these models? If predictions are relatively consistent for similar models, then the standard approach of choosing the model that optimizes a penalized loss suffices. But what if predictions vary significantly for similar models? In machine learning, thi
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Aditya, K. Shastry, M. Mohan, and K. Deepthi. "Hybrid Stacked Ensemble Regression Model for Predicting Parkinson's Progression on Protein Data." CommIT (Communication and Information Technology) Journal 19, no. 1 (2025): 15–27. https://doi.org/10.21512/commit.v19i1.12079.

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Parkinson’s Disease (PD) is a progressive neurological disorder marked by both motor and nonmotor symptoms. Accurate prediction of disease progression is critical for effective patient management. The research presents a Hybrid Stacked Ensemble Regression (HSER) model for predicting PD progression using protein and peptide data measurements, leveraging the Movement Disorder Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDSUPDRS) scores. The researchers integrate three datasets: clinical data, protein data, and peptide data into a comprehensive feature-engineered d
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Asiah, Mat, Khidzir Nik Zulkarnaen, Deris Safaai, Mat Yaacob Nik Nurul Hafzan, Mohamad Mohd Saberi, and Safaai Siti Syuhaida. "A Review on Predictive Modeling Technique for Student Academic Performance Monitoring." MATEC Web of Conferences 255 (2019): 03004. http://dx.doi.org/10.1051/matecconf/201925503004.

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Despite of providing high quality of education, demand on predicting student academic performance become more critical to improve the quality and assisting students to achieve a great performance in their studies. The lack of existing an efficiency and accurate prediction model is one of the major issues. Predictive analytics can provide institution with intuitive and better decision making. The objective of this paper is to review current research activities related to academic analytics focusing on predicting student academic performance. Various methods have been proposed by previous resear
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Widhi, Oktavandi, Maria Safitri, Usman Usman, and Amalia Nur Chasanah. "Comparation Of Bankruptcy Prediction At Retail Companies In Indonesia Using Altman, Zmijewski and Springate Methods." Finance : International Journal of Management Finance 2, no. 2 (2024): 1–10. https://doi.org/10.62017/finance.v2i2.55.

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The Indonesian retail sector, a significant contributor to the nation's GDP, faces challenges due to digital transformation, shifting consumer behavior, and increased competition from e-commerce platforms, leading to potential bankruptcy risks among traditional retailers. This study aims to compare the Altman, Zmijewski, and Springate models in predicting bankruptcy of retail companies listed on the Indonesia Stock Exchange from 2019 to 2023. Using financial data from 11 retail companies, the study calculated bankruptcy predictions using the three models and performed statistical tests (Kolmog
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Dissertations / Theses on the topic "Predicting model"

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Andeta, Jemal Ahmed. "Road-traffic accident prediction model : Predicting the Number of Casualties." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-20146.

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Efficient and effective road traffic prediction and management techniques are crucial in intelligent transportation systems. It can positively influence road advancement, safety enhancement, regulation formulation, and route planning to save living things in advance from road traffic accidents. This thesis considers road safety by predicting the number of casualties if an accident occurs using multiple traffic accident attributes. It helps individuals (drivers) or traffic offices to adjust and control their contributions for the occurrence of an accident before emerging it. Three candidate alg
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li, yiwen. "Predicting Hearing Loss Using Auditory Steady-State Responses." Digital WPI, 2009. https://digitalcommons.wpi.edu/etd-theses/84.

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Auditory Steady-State Response (ASSR) is a promising tool for detecting hearing loss. In this project, we analyzed hearing threshold data obtained from two ASSR methods and a gold standard, pure tone audiometry, applied to both normal and hearing-impaired subjects. We constructed a repeated measures linear model to identify factors that show significant differences in the mean response. The analysis shows that there are significant differences due to hearing status (normal or impaired) and ASSR method, and that there is a significant interaction between hearing status and test signal frequency
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Kingwell, Stephen. "Predicting Complications After Spinal Surgery: Surgeons’ Aided and Unaided Predictions." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41559.

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Despite the emergence of artificial intelligence (AI) and machine learning (ML) in medicine and the resultant interest in predictive analytics in surgery, there remains a paucity of research on the actual impact of prediction models and their effect on surgeons’ risk assessment of post-surgical complications. This research evaluated how spinal surgeons predict post-surgical complications with and without additional information generated by a ML predictive model. The study was conducted in two stages. In the preliminary stage an ML prediction model for post-surgical complications in spine sur
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Degerman, Engfeldt Johnny. "Predicting Electrochromic Smart Window Performance." Licentiate thesis, KTH, Tillämpad elektrokemi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-95167.

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The building sector is one of the largest consumers of energy, where the cooling of buildings accounts for a large portion of the total energy consumption. Electrochromic (EC) smart windows have a great potential for increasing indoor comfort and saving large amounts of energy for buildings. An EC device can be viewed as a thin-film electrical battery whose charging state is manifested in optical absorption, i.e. the optical absorption increases with increased state-of-charge (SOC) and decreases with decreased state-of-charge. It is the EC technology's unique ability to control the absorption
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Barnhart, Gregory J. "Predicting hail size using model vertical velocities." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FBarnhart.pdf.

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Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2008.<br>Thesis Advisor(s): Nuss, Wendell. "March 2008." Description based on title screen as viewed on April 25, 2008. Includes bibliographical references (p. 47-49). Also available in print.
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Sofi, Backman. "A model for predicting robot dresspack damage." Thesis, Umeå universitet, Institutionen för fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149369.

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McClain, Michael Patrick. "A micromechanical model for predicting tensile strength." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-10052007-143117/.

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Gao, Zhiyuan, and Likai Qi. "Predicting Stock Price Index." Thesis, Halmstad University, Applied Mathematics and Physics (CAMP), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-3784.

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<p>This study is based on three models, Markov model, Hidden Markov model and the Radial basis function neural network. A number of work has been done before about application of these three models to the stock market. Though, individual researchers have developed their own techniques to design and test the Radial basis function neural network. This paper aims to show the different ways and precision of applying these three models to predict price processes of the stock market. By comparing the same group of data, authors get different results. Based on Markov model, authors find a tendency of
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Seidu, Mohammed Nazib. "Predicting Bankruptcy Risk: A Gaussian Process Classifciation Model." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119120.

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This thesis develops a Gaussian processes model for bankruptcy risk classification and prediction in a Bayesian framework. Gaussian processes and linear logistic models are discriminative methods used for classification and prediction purposes. The Gaussian processes model is a much more flexible model than the linear logistic model with smoothness encoded in the kernel with the potential to improve the modeling of the highly nonlinear relationships between accounting ratios and bankruptcy risk. We compare the linear logistic regression with the Gaussian process classification model in the con
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Chen, Dong. "Neural network model for predicting performance of projects." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape9/PQDD_0021/MQ48059.pdf.

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Books on the topic "Predicting model"

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Ferguson, Dennis E. Predicting regeneration establishment with the prognosis model. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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Ferguson, Dennis E. Predicting regeneration establishment with the prognosis model. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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Ferguson, Dennis E. Predicting regeneration establishment with the prognosis model. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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United States. National Aeronautics and Space Administration., ed. Development of a model for predicting NASA/MSFC project success. Dept. of Industrial and Systems Engineering, University of Alabama in Huntsville, 1990.

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Ward, S. C. Validation of a CFD model for predicting film cooling performance. American Institute of Aeronautics and Astronautics, 1993.

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Weber, Randal S. A model for predicting transfusion requirements in head and neck surgery. American Laryngological, Rhinological and Otological Society, 1995.

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Maurice, Clark Robert, and Risk Reduction Engineering Laboratory (U.S.), eds. Predicting the inactivation of giardia lamblia: A mathematical and statistical model. U.S. Environmental Protection Agency, Risk Reduction Engineering Laboratory, 1990.

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Eskridge, Robert E. ROADWAY--a numerical model for predicting air pollutants near highways: User's guide. U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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Eskridge, Robert E. ROADWAY--a numerical model for predicting air pollutants near highways: User's guide. U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.

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S, Bhown A., and Hazardous Waste Engineering Research Laboratory, eds. Predicting the effectiveness of chemical-protective clothing: Model and test method development. U.S. Environmental Protection Agency, Hazardous Waste Engineering Research Laboratory, 1986.

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Book chapters on the topic "Predicting model"

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Halbrügge, Marc. "Model-Based UI Development (MBUID)." In Predicting User Performance and Errors. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60369-8_3.

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Khodabandeh, Peyman, Fazel Azarhomayun, Mohammad Shekarchi, and Mahdi Kioumarsi. "Predicting Dry Shrinkage Using Machine Learning Methods." In Lecture Notes in Civil Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-69626-8_61.

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AbstractModeling drying shrinkage presents significant challenges due to the complexity and multitude of contributing parameters. This study provides detailed insights into the input requirements and predictive capabilities of established models by leveraging various datasets from the NU-ITI database. Initially, the performance of a shrinkage model was evaluated. The data for a machine learning random forest model included eight variables, interpreted through SHapley Additive exPlanations (SHAP), which elucidates the most influential inputs. However, the partial dependency graphs yielded minim
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Ahokangas, Petri, Irina Atkova, Seppo Yrjölä, and Marja Matinmikko-Blue. "Business Model Theory and the Becoming of New Mobile Communications Technologies." In Business Model Innovation. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-57511-2_9.

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AbstractExtant research falls short of explaining and predicting business model innovation (BMI) in emerging futuristic technology contexts. Building on next-generation mobile communications technology (6G), this research develops and explicates a forward-looking business model theory. It explains and theorizes BMI by systematically linking opportunity with scalability, value with sustainability, and advantage with replicability as antecedents and outcomes. The developed business model theory shifts the focus from the firm towards an ecosystem level of analysis and expands the time continuum f
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Wasserman, Theodore, and Lori Wasserman. "Predicting Errors and Motivation." In Motivation, Effort, and the Neural Network Model. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58724-6_6.

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McMillan, David G. "Forecast and Market Timing Power of the Model and the Role of Inflation." In Predicting Stock Returns. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69008-7_6.

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Caffaro, Fabio, Lorenzo Bongiovanni, and Claudio Rossi. "Geo-temporal Crime Forecasting Using a Deep Learning Attention-Based Model." In Security Informatics and Law Enforcement. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62083-6_26.

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AbstractCrime prediction is a crucial problem in law enforcement, and the ability to forecast where and when crimes are likely to occur can help police departments allocate their resources effectively and prevent crimes. In this chapter, we propose a geo-temporal crime forecasting model based on a transformer architecture. We use a public dataset from the Boston Police Department and forecast crimes in each cell of a 1 km × 1 km grid. We use an encoder–decoder structure to capture the spatiotemporal patterns of the crimes. The encoder elaborates the crimes that occurred in each cell during the
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Balaniuk, Remis, Hercules Antonio do Prado, Renato da Veiga Guadagnin, Edilson Ferneda, and Paulo Roberto Cobbe. "Predicting Evasion Candidates in Higher Education Institutions." In Model and Data Engineering. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24443-8_16.

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Qian, Shenghua. "Vehicle Collision Prediction Model on the Internet of Vehicles." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_53.

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AbstractAn active collision prediction model on the Internet of Vehicles is proposed. Through big data calculation on the cloud computing platform, the model predicts whether the vehicles may collide and the time of the collision, so the server actively sends warning signals to the vehicles that may collide. Firstly, the vehicle collision prediction model preprocesses the data set, and then constructs a new feature set through feature engineering. For the imbalance of the data set, which affects predictive results, SMOTE algorithm is proposed to generate new samples. Then, the LightGBM algorit
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Zhang, Caizhen, Zongzhi Li, and Zaixing Wang. "Getting Effect Prediction Model of CVTPD-PSL Technology Based on Artificial Neural Network." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2409-6_38.

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Abstract In view of technology characteristic of Continuously Variable Phosphorous Getting Process Using a Porous Silicon Layer (PSL-CVTPDG), a prediction model based on Artificial Neural Network(ANN) is put forward for predicting effect of PSL-CVTPDG process. Establish, train, test and verify as long as simulation of the prediction model were finished by means of ANN function in MATLAB. Experimental results show that the prediction and actually measured values are very close to, the output follows the tracks of the expectation value very well, which reflects that the ANN is an effective metho
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Antweiler, Dario, Jan Pablo Burgard, Marc Harmening, et al. "A Regression-Based Predictive Model Hierarchy for Nonwoven Tensile Strength Inference." In Cognitive Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83097-6_4.

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Abstract Nonwoven materials, characterized by a random fiber structure, are essential for various applications including insulation and filtering. An industrial long-term goal is to establish a framework for the simulation-based design of nonwovens. Due to the random structures, simulations of material properties on fiber network level are computational expensive. We propose a predictive model hierarchy for inferring an important material property—the nonwoven tensile strength behavior. The model hierarchy is built using regression-based approaches, including linear and polynomial models, whic
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Conference papers on the topic "Predicting model"

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Santana, Vinicius V., Carine M. Rebello, Erbet A. Costa, et al. "Recurrent Deep Learning Models for Multi-step Ahead Prediction: Comparison and Evaluation for Real Electrical Submersible Pump (ESP) System." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.107762.

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Predicting processes� future behavior based on past data is vital for automatic control and dynamic optimization in engineering. Recent advances in deep learning, particularly Artificial Neural Networks, have improved predictions in various engineering fields. Recurrent Neural Networks (RNNs) are well-suited for time series data, as they naturally evolve through dynamic systems with recurrent updates. Despite their high predictive power, RNNs may underperform if their training ignores the model's future application. In Model Predictive Control, for example, the model evolves over time using on
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Suresh Dahake, Parihar, and Nihar Suresh Dahake. "Predicting Buyer Behaviour: A Reconnaissance of Retail Predictive Analytics Model." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007411.

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Park, Jiwoo, Harin Min, Yejin Kim, Seoyeong Ahn, and Whanhee Lee. "Machine learning Model, predicting South Korea precipitation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822089.

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Blackburne, P. N., and R. B. Griffin. "Mathematical Model for Predicting Calcareous Film Formation." In CORROSION 1996. NACE International, 1996. https://doi.org/10.5006/c1996-96562.

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Abstract A previously developed mathematical model describing the formation of calcareous deposits on cathodically protected steel in seawater has been improved. By entering geographical coordinates of interest and a time duration, current density requirements and surface coverage of calcareous deposits as a function of time and depth are predicted. From this data, surface plots of current density requirements and calcareous deposit coverage can be easily created. The data can also be used to construct polarization curves, which can be used as input into a cathodic protection model for calcula
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Anugraha, David, Genta Indra Winata, Chenyue Li, Patrick Amadeus Irawan, and En-Shiun Annie Lee. "ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models." In Findings of the Association for Computational Linguistics: NAACL 2025. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.findings-naacl.106.

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Knight, Michael, Ghousia Saeed, Yu-Horng Chen, and Andre G. P. Brown. "Remote Location in an Urban Digital Model." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.581.

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Huang, Chuen-huei (Joseph), and Robert J. Krawczyk. "A Choice Model of Consumer Participatory Design for Modular Houses." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.679.

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Huang, Chuen-huei (Joseph), and Robert J. Krawczyk. "A Choice Model of Consumer Participatory Design for Modular Houses." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.679.

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Pan, Tao, Zhen Li, Chengkai Zhang, et al. "Predicting Rate of Penetration Using the Dual Seq2Seq Model." In International Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/igs-2023-0353.

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Abstract Machine learning models have made significant strides in efficiently and accurately predicting rate of penetration (ROP) recently. Traditional machine learning models primarily rely on time-series parameters like the weight on bit, et al. while often overlooking non time-series features such as tool combinations. This study proposes an intelligent ROP prediction method based on a dual-input sequence-to-sequence model (Dual-S2S), which simultaneously considers both temporal and non-temporal factors to achieve accurate ROP predictions. First, time-series features, as well as non-time-se
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Schlueter, Arno, and Tobias Bonwetsch. "The M.ANY Project - Exploring a Matrix Model for a Fully Digital Workflow in Architectural Design." In eCAADe 2007: Predicting the Future. eCAADe, 2007. http://dx.doi.org/10.52842/conf.ecaade.2007.895.

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Reports on the topic "Predicting model"

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Barnes, Graham. Predicting Flare Properties Using the Minimum Current Corona Model. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada503355.

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Scharine, Angelique A., Paula P. Henry, Mohan D. Rao, and Jason T. Dreyer. A Model for Predicting Intelligibility of Binaurally Perceived Speech. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada466840.

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Levine, Daniel B., John J. Cloos, James Perry, Thomas C. Varley, and Stanley A. Horowitz. A Model for Predicting the Inventory of Navy Spares. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada243087.

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Gómez Loscos, Ana, Miguel Ángel González Simón, and Matías José Pacce. Short-term real-time forecasting model for spanish GDP (Spain-STING): new specification and reassessment of its predictive power. Banco de España, 2024. http://dx.doi.org/10.53479/36137.

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The predictive power of short-term forecasting models was impaired by the increased volatility observed in most economic indicators following the outbreak of COVID-19. This paper sets out a revision of the Spain-STING model (one of the tools used by the Banco de España for short-term forecasts of quarter-on-quarter GDP growth) with a view to improving its predictive power in the wake of the pandemic. In particular, the revision entails three main changes: (i) the correlation between the indicators included in the model and the estimated common component is now coincident for all of the indicat
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Dunn, Stuart, Douglas Coats, Gary Nickerson, Samuel Sopok, and Peter O'Hara. Unified Computer Model for Predicting Thermochemical Erosion in Gun Barrels. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada420028.

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Allen, D. H., and W. E. Haisler. A Model for Predicting Thermomechanical Response of Large Space Structures. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162139.

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Allen, D. H., and W. E. Haisler. A Model for Predicting Thermomechanical Response of Large Space Structures. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada172966.

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O’Keeffe, Hannah, and Katerina Petrova. Component-Based Dynamic Factor Nowcast Model. Federal Reserve Bank of New York, 2025. https://doi.org/10.59576/sr.1152.

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In this paper, we propose a component-based dynamic factor model for nowcasting GDP growth. We combine ideas from “bottom-up” approaches, which utilize the national income accounting identity through modelling and predicting sub-components of GDP, with a dynamic factor (DF) model, which is suitable for dimension reduction as well as parsimonious real-time monitoring of the economy. The advantages of the new model are twofold: (i) in contrast to existing dynamic factor models, it respects the GDP accounting identity; (ii) in contrast to existing “bottom-up” approaches, it models all GDP compone
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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2119.

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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25
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