Relevant bibliographies by topics / Predictive estimate

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

Academic literature on the topic 'Predictive estimate'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Predictive estimate"

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Tansitpong, Praowpan. "Probabilistic Model of Patient Classification Using Bayesian Model." International Journal of Reliable and Quality E-Healthcare 13, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijrqeh.348579.

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The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on sta
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Fung, Juan F., Siamak Sattar, David T. Butry, and Steven L. McCabe. "A predictive modeling approach to estimating seismic retrofit costs." Earthquake Spectra 36, no. 2 (2020): 579–98. http://dx.doi.org/10.1177/8755293019891716.

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This article presents a methodology for estimating seismic retrofit costs from historical data. In particular, historical retrofit-cost data from Federal Emergency Management Agency (FEMA) 156 is used to build a generalized linear model (GLM) to predict retrofit costs as a function of building characteristics. While not as accurate as an engineering professional’s estimate, this methodology is easy to apply to generate quick estimates and is especially useful for decision makers with large building portfolios. Moreover, the predictive modeling approach provides a measure of uncertainty in term
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Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (2021): 932. http://dx.doi.org/10.3390/s21030932.

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipme
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Amry, Zul. "Bayesian Estimate of Parameters for ARMA Model Forecasting." Tatra Mountains Mathematical Publications 75, no. 1 (2020): 23–32. http://dx.doi.org/10.2478/tmmp-2020-0002.

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AbstractThis paper presents a Bayesian approach to finding the Bayes estimator of parameters for ARMA model forecasting under normal-gamma prior assumption with a quadratic loss function in mathematical expression. Obtaining the conditional posterior predictive density is based on the normal-gamma prior and the conditional predictive density, whereas its marginal conditional posterior predictive density is obtained using the conditional posterior predictive density. Furthermore, the Bayes estimator of parameters is derived from the marginal conditional posterior predictive density.
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Carpenter, Chris. "Decision Tree Regressions Estimate Liquid Holdup in Two-Phase Gas/Liquid Flows." Journal of Petroleum Technology 73, no. 11 (2021): 75–76. http://dx.doi.org/10.2118/1121-0075-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203448, “Decision-Tree Regressions for Estimating Liquid Holdup in Two-Phase Gas/Liquid Flows,” by Meshal Almashan, SPE, Yoshiaki Narusue, and Hiroyuki Morikawa, University of Tokyo, prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually 9–12 November. The paper has not been peer reviewed. In the authors’ study, a machine-learning predictive model—boosted decision tree regression (BDTR)—is trained, tested, and evaluated in predicting liquid hold
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GX-Chen, Anthony, Veronica Chelu, Blake A. Richards, and Joelle Pineau. "A Generalized Bootstrap Target for Value-Learning, Efficiently Combining Value and Feature Predictions." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6829–37. http://dx.doi.org/10.1609/aaai.v36i6.20639.

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Estimating value functions is a core component of reinforcement learning algorithms. Temporal difference (TD) learning algorithms use bootstrapping, i.e. they update the value function toward a learning target using value estimates at subsequent time-steps. Alternatively, the value function can be updated toward a learning target constructed by separately predicting successor features (SF)—a policy-dependent model—and linearly combining them with instantaneous rewards. We focus on bootstrapping targets used when estimating value functions, and propose a new backup target, the ?-return mixture,
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Li, Yun, Xiangfeng Yin, and Xueting Wen. "SETAR Model Research Based on Support Vector Regression Parameter Estimation Method and Empirical Analysis." Journal of Physics: Conference Series 2216, no. 1 (2022): 012093. http://dx.doi.org/10.1088/1742-6596/2216/1/012093.

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Abstract In this paper, we use support vector regression (SVR) to estimate the parameters of the SETAR model for predicting the log-return of stock price data. SETAR model is usually estimated using maximum likelihood (ML) and minimize conditional sum-of-squares (CSS), assuming that the data are normal distribution. SVR is a powerful tool for regression estimation. Therefore, this paper proposes to use SVR instead of maximum likelihood and conditional least squares to estimate the parameters of the SETAR model. Empirical analysis on the two stock price data of China Unicom and ICBC, the result
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Rust, Nicole C., and Stephanie E. Palmer. "Remembering the Past to See the Future." Annual Review of Vision Science 7, no. 1 (2021): 349–65. http://dx.doi.org/10.1146/annurev-vision-093019-112249.

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In addition to the role that our visual system plays in determining what we are seeing right now, visual computations contribute in important ways to predicting what we will see next. While the role of memory in creating future predictions is often overlooked, efficient predictive computation requires the use of information about the past to estimate future events. In this article, we introduce a framework for understanding the relationship between memory and visual prediction and review the two classes of mechanisms that the visual system relies on to create future predictions. We also discus
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Kopec, Jacek A. "Estimating Disease Prevalence in Administrative Data." Clinical and Investigative Medicine 45, no. 2 (2022): E21–27. http://dx.doi.org/10.25011/cim.v45i2.38100.

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Purpose: Disease prevalence estimates from population-based administrative databases are often biased due to measurement (misclassification) errors. The purpose of this article is to review the methodology for estimating disease prevalence in administrative data, with a focus on bias correction. Source: Several approaches to bias correction in administrative data were reviewed and application of these methods was demonstrated using an example from the literature: physician claims and hospitalization data were employed to estimate diabetes prevalence in Ontario, Canada. Findings: Misclassificat
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Vehtari, Aki, and Jouko Lampinen. "Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities." Neural Computation 14, no. 10 (2002): 2439–68. http://dx.doi.org/10.1162/08997660260293292.

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In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate because it describes the uncertainty in the estimate. The distributions of the expected utility estimates can also be used to compare models, for example, by computing the probability of one model having a better expected
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Dissertations / Theses on the topic "Predictive estimate"

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Kromphardt, Benjamin D. "Aircraft ground damage and the use of predictive models to estimate costs." Thesis, California State University, Long Beach, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527013.

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<p> Aircraft are frequently involved in ground damage incidents, and repair costs are often accepted as part of doing business. The Flight Safety Foundation (FSF) estimates ground damage to cost operators $5-10 billion annually. Incident reports, documents from manufacturers or regulatory agencies, and other resources were examined to better understand the problem of ground damage in aviation. Major contributing factors were explained, and two versions of a computer-based model were developed to project costs and show what is possible. One objective was to determine if the models could match t
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Fernández, Marcos Daniel. "Predictive modelling applied to estimate demand on new EV charging stations in the UK." Thesis, KTH, Kraft- och värmeteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293215.

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The arrival of the electric vehicle is being faster than estimated. This growth requires a charging infrastructure that can cover the energy needs of electric mobility. To this end, it is necessary that the charging points be installed in the most suitable places to satisfy the coming demand. The optimal and efficient choice of the location of new charging sites will not only help to absorb more demand, but will also improve the economic prospect and facilitate the adoption of electric vehicles. Tesla is the fastest growing electric vehicle company in recent years, with over 500,000 EVs worldw
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Colmanetti, Michel Anderson Almeida. "Aboveground biomass of Atlantic Forest: modeling and strategies for carbon estimate." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/91/91131/tde-02082018-095010/.

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The current concerning on potential effect of CO2 on climate change has assigned to the biomass of the tropical forest the importance as a sink of carbon. However, the heterogeneity of the natural ecosystems in tropics has significant implications for biomass estimation. This study proposed different biomass models using destructive sampling for the highly diverse Atlantic Forest. Models from two different approaches: generalized and species-specific were fitted and had the performance compared. Regarding the generalized models, it was proposed different covariates including diameter at breast
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Дмитрик, Ю. В. "Прогнозна оцінка факторів формування чистого прибутку банку". Thesis, Українська академія банківської справи Національного банку України, 2012. http://essuir.sumdu.edu.ua/handle/123456789/63335.

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В ринковій економіці одним з показників, який свідчить про початок кризи в діяльності банку, є скорочення чистого прибутку. Важливе значення чистого прибутку обумовлене тим, що він є вагомим елементом приросту власного капіталу, який гарантує фінансову стійкість банку та ліквідність його балансу, виступає базою для нарощення та оновлення основних фондів, забезпечення відповідного рівня дивідендів, розвитку та підвищення якості банківських послуг.
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Francois, Clément, Philippe Laramée, Nora Rahhali, et al. "A Predictive Microsimulation Model to Estimate the Clinical Relevance of Reducing Alcohol Consumption in Alcohol Dependence." Karger, 2014. https://tud.qucosa.de/id/qucosa%3A71614.

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Background: Alcohol consumption is one of the most important factors for disease and disability in Europe. In clinical trials, nalmefene has resulted in a significant reduction in the number of heavy-drinking days (HDDs) per month and total alcohol consumption (TAC) among alcohol-dependent patients versus placebo. Methods: A microsimulation model was developed to estimate alcohol-attributable diseases and injuries in patients with alcohol dependence and to explore the clinical relevance of reducing alcohol consumption. Results: For all diseases and injuries considered, the number of events (in
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Evangelista, Nathan Sombra. "Development of a method for predictive calculations density of ionic liquids in a wide range of temperature and pressure." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13986.

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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior<br>Ionic liquids are compounds of considerable interest due to their unique physicochemical properties. A detailed knowledge of these properties is of great importance. In particular, the liquid density is a very important property required in many design problems and, therefore, in process simulation . Experimental measurements of this property is not viable for all the existing ionic liquids . Therefore, development of new methods for its estimation is essential. In this work , a new group contribution m
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Yapar, Taner. "A Study Of The Predictive Validity Of The Baskent University English Proficiency Exam Through The Use Of The Two-parameter Irt Model&amp." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1217629/index.pdf.

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The purpose of this study is to analyze the predictive power of the ability estimates obtained through the two-parameter IRT model on the English Proficiency Exam administered at BaSkent University in September 2001 (BUSPE 2001). As prerequisite analyses the fit of one- and two-parameter models of IRT were investigated. The data used for this study were the test data of all students (727) who took BUSPE 2001 and the departmental English course grades of the passing students. At the first stage, whether the assumptions of IRT were met was investigated. Next, the observed and theoretical distrib
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Townsend, Daphne. "Clinical trial of estimated risk stratification prediction tool." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27926.

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This work presents doctors with a model of the estimated degree of risk of rare and important neonatal outcomes to aid in better decisions and improved allocation of equipment and resources. An extensive list of admission day parameters is reduced to minimum variable sets to create models for outcomes that are relevant to decision-making in the neonatal intensive care unit. Models are applied to a special collection of cases and compared to neonatologists' risk estimates. A comparative analysis of physician's predictions and the models' discrimination abilities highlights areas of success and
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Zbede, Yaman. "Model predictive MRAS estimator for sensorless induction motor drives." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3771.

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The project presents a novel model predictive reference adaptive system (MRAS) speed observer for sensorless induction motor drives applications. The proposed observer is based on the finite control set-model predictive control principle. The rotor position is calculated using a search-based optimization algorithm which ensures a minimum speed tuning error signal at each sampling period. This eliminates the need for a proportional integral (PI) controller which is conventionally employed in the adaption mechanism of MRAS observers. Extensive simulation and experimental tests have been carried
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Hossan, Md Shakawat. "Prediction Model to Estimate the Zero Crossing Point for Faulted Waveforms." UKnowledge, 2014. http://uknowledge.uky.edu/ece_etds/53.

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In any power system, fault means abnormal flow of current. Insulation breakdown is the cause of fault generation. Different factors can cause the breakdown: Wires drifting together in the wind, Lightning ionizing air, wires with contacts of animals and plants, Salt spray or pollution on insulators. The common type of faults on a three phase system are single line-to-ground (SLG), Line-to-line faults (LL), double line-to-ground (DLG) faults, and balanced three phase faults. And these faults can be symmetrical (balanced) or Unsymmetrical (imbalanced).In this Study, a technique to predict the zer
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Books on the topic "Predictive estimate"

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University of Texas at Austin. Construction Industry Institute. Improving Early Estimates Research Team., ed. Quantitative prediction of estimate accuracy. Construction Industry Institute, University of Texas at Austin, 1999.

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Hjalmarsson, Erik. Interpreting long-horizon estimates in predictive regressions. Federal Reserve Board, 2008.

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Harris, Ian Richard. Smooth and predictive estimates for the compound Poisson distribution. University of Birmingham, 1987.

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Project, Central United States Earthquake Preparedness. Estimated future earthquake losses for St. Louis City and County, Missouri. Federal Emergency Management Agency, 1990.

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Wright, D. E. Fitting predictive accident models in GLIM with uncertainty in the flow estimates. Transport and Road Research Laboratory, 1991.

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Leontaritis, I. J. A prediction error estimator for nonlinear stochastic systems. University, Dept. of Control Engineering, 1986.

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United States. Federal Emergency Management Agency, ed. Estimated future earthquake losses for St. Louis City and County Missouri: Executive summary. Federal Emergency Management Agency, 1997.

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Chen, S. A recursive prediction error parameter estimator for nonlinear models. University of Sheffield, Dept. of Control Engineering, 1988.

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Barrows, Linda H. Comparison of total body water estimates from 18O and bioelectrical response prediction equations. Lyndon B. Johnson Space Center, 1993.

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H, Barrows Linda, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Comparison of total body water estimates from ℗£ı́O and bioelectrical response prediction equations. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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Book chapters on the topic "Predictive estimate"

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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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Fani Sani, Mohammadreza, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. "Event Log Sampling for Predictive Monitoring." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_12.

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AbstractPredictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity predictio
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Kreinovich, Vladik, and Thongchai Dumrongpokaphan. "How to Estimate Statistical Characteristics Based on a Sample: Nonparametric Maximum Likelihood Approach Leads to Sample Mean, Sample Variance, etc." In Predictive Econometrics and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_11.

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Shoush, Mahmoud, and Marlon Dumas. "Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_14.

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AbstractPrescriptive process monitoring is a family of techniques to optimize the performance of a business process by triggering interventions at runtime. Existing prescriptive process monitoring techniques assume that the number of interventions that may be triggered is unbounded. In practice, though, interventions consume resources with finite capacity. For example, in a loan origination process, an intervention may consist of preparing an alternative loan offer to increase the applicant’s chances of taking a loan. This intervention requires time from a credit officer. Thus, it is not possi
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Hair, Joseph F., G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt, Nicholas P. Danks, and Soumya Ray. "An Introduction to Structural Equation Modeling." In Classroom Companion: Business. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80519-7_1.

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AbstractStructural equation modeling is a multivariate data analysis method for analyzing complex relationships among constructs and indicators. To estimate structural equation models, researchers generally draw on two methods: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM). Whereas CB-SEM is primarily used to confirm theories, PLS represents a causal–predictive approach to SEM that emphasizes prediction in estimating models, whose structures are designed to provide causal explanations. PLS-SEM is also useful for confirming measurement models. This chapter offers a conci
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Yang, Chunsheng, Qingfeng Lou, Jie Liu, Hongyu Gou, and Yun Bai. "Particle Filter-Based Approach to Estimate Remaining Useful Life for Predictive Maintenance." In Current Approaches in Applied Artificial Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19066-2_67.

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Cacuci, Dan Gabriel, and Mihaela Ionescu-Bujor. "Sensitivity and Uncertainty Analysis, Data Assimilation, and Predictive Best-Estimate Model Calibration." In Handbook of Nuclear Engineering. Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-98149-9_17.

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Ono, Kohei, Shuichi Kurogi, and Takeshi Nishida. "Moments of Predictive Deviations for Ensemble Diversity Measures to Estimate the Performance of Time Series Prediction." In Neural Information Processing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_8.

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Bouasria, Abdelkrim, Khalid Ibno Namr, Abdelmejid Rahimi, and El Mostafa Ettachfini. "Estimate Soil Organic Matter from Remote Sensing Data by Using Statistical Predictive Models." In Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90633-7_98.

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Zhuma, Emilio, José Luis Tubay, Byron Oviedo, and Cristian Zambrano-Vega. "Predictive Model to Estimate the Weight in Native Fish Based on Physiological Parameters." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87704-9_25.

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Conference papers on the topic "Predictive estimate"

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Zapp, Philip E., and Thomas B. Edwards. "Statistical Analysis of Inhibitor Concentrations for Radioactive Waste in Carbon Steel Tanks." In CORROSION 1994. NACE International, 1994. https://doi.org/10.5006/c1994-94116.

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Abstract Based on a logistic regression approach, a model was developed using the explanatory variables log([NO3-]), log([NO2-]), and temperature to estimate the probability of pitting in a carbon steel exposed to high-level radioactive waste. Pitting susceptibility data obtained by the two techniques of cyclic potentiodynamic polarization and coupon immersion were separately and jointly analyzed with the model. Similar predictive ability is seen for equations based on both electrochemical and coupon immersion data. Using the theory associated with the determination of confidence intervals for
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Del, Caroline, Konstanca Nikolajevic, Julien Denoulet, Bertrand Granado, Christophe Marsala, and Frederic Beroul. "Direct Load Recognition Application to Main Rotor Pitch-Link Load on the H175 Fleet: A New Wavelet Approach." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0080-2024-1205.

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This paper presents a new approach, variant of the Direct Load Recognition (DLR) methodology, to estimate the main rotor (MR) pitch-link load on customer flights. The original DLR methodology is based on the combination of a harmonic decomposition and the use of Machine Learning algorithms. The DLR variant replaces the harmonic decomposition by a wavelet decomposition. The application of this paper consists in two parts. First, the comparison between the original DLR and DLR variant on prototype flight test data. Two results are highlighted in this part. The capacity of representation of the p
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Ngo, Tri, and Cornel Sultan. "Towards Automation of Helicopter Landings on Ship Decks Using Integer Programming and Model Predictive Control." In Vertical Flight Society 80th Annual Forum & Technology Display. The Vertical Flight Society, 2024. http://dx.doi.org/10.4050/f-0074-2018-12783.

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A novel method for the automated control of helicopters in landing maneuvers on ship decks is proposed, which combines integer programming and model predictive control (MPC). The helicopter is first brought sufficiently close to the landing deck using a standard MPC. In the final phase of the mission, termed the rendezvous phase, implementation of the novel control design method allows the MPC to rapidly adapt to the landing deck state via a variable prediction horizon. The control design problem includes an integer variable vector which is used to frequently estimate an appropriate prediction
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Wilson, P. T., and S. Harjac. "Predictive Modeling of Carbamate Formation in Ammonia Recovery Stills." In CORROSION 2007. NACE International, 2007. https://doi.org/10.5006/c2007-07195.

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Abstract The recovery of ammonia and carbon dioxide from leaching solutions in a nickel refinery contributes to the economics of the process and to reducing the environmental impact of waste streams. The ammonia and carbon dioxide recovery process involves the counter current contact between steam and the process and tailings streams in steam stripping columns. The recovered stream consists of a mixture of water vapour, carbon dioxide and ammonia at a pressure of 125 kPa and having a temperature range of 50 to 90°C. Energy recovery heat exchangers are part of the ammonia and carbon dioxide rec
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Lu, Baotong, and Hui Yu. "Case Study - AC Mitigation Design for Multiple Pipelines without Powerline Data." In CONFERENCE 2024. AMPP, 2024. https://doi.org/10.5006/c2024-20632.

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Abstract This case study demonstrates the applicability of the AC Interference Mitigation (ACIM) design method (reported in our preceding paper, C2023-18983) under the conditions of unavailable power data for collocated powerlines and for multiple pipelines that are electrically connected. It was assumed that field AC voltage readings were collected from test stations along at least one pipeline. The procedure is as follows: Estimate the power loads of the powerlines using a trial-and-error method via simulating the AC voltage profile(s) of the pipeline.Validate the predictive model that is ba
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Al-Zubaidi, Wisam Haitham Abbood, Hoa Khanh Dam, Aditya Ghose, and Xiaodong Li. "Multi-objective search-based approach to estimate issue resolution time." In PROMISE: Predictive Modelling in Software Engineering. ACM, 2017. http://dx.doi.org/10.1145/3127005.3127011.

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Albistur, Cristian, Pablo Aravena, Luis Moran, and Jose Espinoza. "A simple predictive method to estimate flicker." In 2013 IEEE Industry Applications Society Annual Meeting. IEEE, 2013. http://dx.doi.org/10.1109/ias.2013.6682605.

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Xue, Feng, Runle Du, and Jiaqi Liu. "A recursive predictive risk estimate for proximal algorithms." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472528.

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Bedi, Vinay Kumar, Kashif Rana, Pundarik Mahata, Prasenjit Ghosh, and Rabindra Mukhopadhyay. "A Predictive Approach to Estimate Tyre Wear Characteristics." In Symposium on International Automotive Technology. SAE International, 2024. http://dx.doi.org/10.4271/2024-26-0313.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Tyre wear is of significant concern for the automotive industry due to multiple reasons including vehicle performance, safety, economy, environmental (particulate matter emission) aspects, etc. Therefore, ensuring enhanced tyre tread wear resistance is one of the most important criteria while developing a new tyre. Tyre wear phenomenon is influenced by various factors, such as road conditions, driving habits, maintenance practices and tyre design parameters (construction, geometry and material). The wear assessment throu
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Ferrucci, Filomena, Carmine Gravino, and Federica Sarro. "Exploiting prior-phase effort data to estimate the effort for the subsequent phases." In PROMISE '14: The 10th International Conference on Predictive Models in Software Engineering. ACM, 2014. http://dx.doi.org/10.1145/2639490.2639509.

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Reports on the topic "Predictive estimate"

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Méndez-Vizcaíno, Juan C., Alexander Guarín, César Anzola-Bravo, and Anderson Grajales-Olarte. Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia. Banco de la República, 2021. http://dx.doi.org/10.32468/be.1178.

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Since July 2021, Banco de la República strengthened its forecasting process and communication instruments, by involving predictive densities on the projections of its models, PATACON and 4GM. This paper presents the main theoretical and empirical elements of the predictive density approach for macroeconomic forecasting. This model-based methodology allows to characterize the balance of risks of the economy, and quantify their effects through a joint probability distribution of forecasts. We estimate this distribution based on the simulation of DSGE models, preserving the general equilibrium re
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Ndoye, Aïssatou, Khadim Dia, and Racine Ly. AAgWa Crop Production Forecasts Brief Series - Issue N.04. AKADEMIYA2063, 2023. http://dx.doi.org/10.54067/acpf.04.

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The Africa Agriculture Watch (AAgWa) Crop Production Forecast Brief 4 by AKADEMIYA2063 seeks to provide accurate production levels in Burkina Faso using the Africa Food Crop Production (AfCP) model. The AfCP, developed by AKADEMIYA2063, is an artificial intelligence (AI) based predictive model applied to remotely sensed bio-geophysical data to estimate crop yields and harvests before the harvesting period for nine crops across 47 African countries.
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Allison, Stephen C., Bruce S. Cohen, Edward J. Zambraski, Mark Jaffrey, and Robin Orr. Predictive Models to Estimate Probabilities of Injuries, Poor Physical Fitness, and Attrition Outcomes in Australian Defense Force Army Recruit Training. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ad1000577.

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Fisker, Peter. High-Resolution Estimation of Rural Poverty: Insights from Ethiopia. Data and Evidence to End Extreme Poverty, 2024. https://doi.org/10.55158/deepwp29.

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High-resolution satellite imagery and machine learning techniques enable precise mapping of rural poverty, as demonstrated in this paper for the case of Ethiopia. Using household survey data with precise GPS coordinates, combined with multiple geospatial datasets, we develop a novel two-stage Random Forest model that first predicts roof materials from satellite imagery and then uses this prediction alongside other geospatial features to estimate household consumption levels. The model achieves high predictive accuracy across different spatial scales, with Spearman correlations between 0.81 and
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Carey, Ashley, Madeleine Roberson, Isaac Howard, and Jameson Shannon. Toward a method to predict thermo-mechanical properties of high-strength concrete placements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49660.

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In this article, the merits of a thermo-mechanical framework to estimate properties of high-strength concrete are evaluated for potential standardization as a test method. Previous work conducted by the authors was summarized to show the individual advancements toward development of a laboratory testing framework. Most notably, laboratory-based curing protocols have been shown to produce temperature profiles that were similar to mass placements and achieving peak temperatures that were within 2°C of peak temperatures recorded in a mass high-strength concrete placement. Additionally, current te
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Vas, Dragos, Elizabeth Corriveau, Lindsay Gaimaro, and Robyn Barbato. Challenges and limitations of using autonomous instrumentation for measuring in situ soil respiration in a subarctic boreal forest in Alaska, USA. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/48018.

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Subarctic and Arctic environments are sensitive to warming temperatures due to climate change. As soils warm, soil microorganisms break down carbon and release greenhouse gases such as methane (CH₄) and carbon dioxide (CO₂). Recent studies examining CO₂ efflux note heterogeneity of microbial activity across the landscape. To better understand carbon dynamics, our team developed a predictive model, Dynamic Representation of Terrestrial Soil Predictions of Organisms’ Response to the Environment (DRTSPORE), to estimate CO₂ efflux based on soil temperature and moisture estimates. The goal of this
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Zhang, Caiyun, David Brodylo, Mizanur Rahman, Md Atiqur Rahman, Thomas Douglas, and Xavier Comas. Using an object-based machine learning ensemble approach to upscale evapotranspiration measured from eddy covariance towers in a subtropical wetland. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48056.

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Accurate prediction of evapotranspiration (ET) in wetlands is critical for understanding the coupling effects of water, carbon, and energy cycles in terrestrial ecosystems. Multiple years of eddy covariance (EC) tower ET measurements at five representative wetland ecosystems in the subtropical Big Cypress National Preserve (BCNP), Florida (USA) provide a unique opportunity to assess the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) ET operational product MOD16A2 and upscale tower measured ET to generate local/regional wetland ET maps. We developed an object-based mac
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Mahajan, Aprajit, Shekhar Mittal, Ofir Reich, and Taha Barwahwala. Using Machine Learning to Catch Bogus Firms. Institute of Development Studies, 2024. http://dx.doi.org/10.19088/ictd.2024.050.

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We investigate the use of a machine learning algorithm to identify non-existent(fraudulent) firms that are used for tax evasion. Using a rich dataset of tax returns in an Indian state over several years, we train a machine learning-based model to predict fraudulent firms. We then use the model predictions to carry out field inspections of firms identified as suspicious by the machine learning tool. We find that the machine learning model is accurate in both simulated and field settings in identifying non-existent firms. Withholding a randomly selected group of firms from inspection, we estimat
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Reyes, Celia, Connie Bayudan-Dacuycuy, Michael Abrigo, et al. PIDS-BSP Annual Macroeconometric Model for the Philippines: Preliminary Estimates and Ways Forward. Philippine Institute for Development Studies, 2020. https://doi.org/10.62986/dp2020.16.

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Given new programs and policies in the Philippines, there is a need to formulate a macroeconometric model (MEM) to gain more insights on how the economy and its sectors are affected. This paper discusses the estimation of an annual MEM that will be used for policy analysis and forecasting with respect to the opportunities and challenges brought about by new developments. The formulation of an annual MEM is useful in assisting major macroeconomic stakeholder, such as the National Economic and Development Authority and the Bangko Sentral ng Pilipinas (BSP) in their conduct of policy simulations,
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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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