Relevant bibliographies by topics / Flexible regression models

Contents

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

Academic literature on the topic 'Flexible regression models'

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

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Journal articles on the topic "Flexible regression models"

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Gurmu, Shiferaw, and John Elder. "Flexible Bivariate Count Data Regression Models." Journal of Business & Economic Statistics 30, no. 2 (2012): 265–74. http://dx.doi.org/10.1080/07350015.2011.638816.

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O'Donnell, David, Alastair Rushworth, Adrian W. Bowman, E. Marian Scott, and Mark Hallard. "Flexible regression models over river networks." Journal of the Royal Statistical Society: Series C (Applied Statistics) 63, no. 1 (2013): 47–63. http://dx.doi.org/10.1111/rssc.12024.

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Nikulin, M., and Hong-Dar Isaac Wu. "Flexible regression models for carcinogenesis studies." Journal of Mathematical Sciences 145, no. 2 (2007): 4880–93. http://dx.doi.org/10.1007/s10958-007-0322-z.

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Lee, Young K., Enno Mammen, and Byeong U. Park. "Flexible generalized varying coefficient regression models." Annals of Statistics 40, no. 3 (2012): 1906–33. http://dx.doi.org/10.1214/12-aos1026.

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Durrleman, Sylvain, and Richard Simon. "Flexible regression models with cubic splines." Statistics in Medicine 8, no. 5 (1989): 551–61. http://dx.doi.org/10.1002/sim.4780080504.

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Bonat, Wagner Hugo, and Célestin C. Kokonendji. "Flexible Tweedie regression models for continuous data." Journal of Statistical Computation and Simulation 87, no. 11 (2017): 2138–52. http://dx.doi.org/10.1080/00949655.2017.1318876.

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Dahl, Christian M., and Svend Hylleberg. "Flexible regression models and relative forecast performance." International Journal of Forecasting 20, no. 2 (2004): 201–17. http://dx.doi.org/10.1016/j.ijforecast.2003.09.002.

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Santías, Francisco Reyes, Carmen Cadarso-Suárez, and María Xosé Rodríguez-Álvarez. "Estimating hospital production functions through flexible regression models." Mathematical and Computer Modelling 54, no. 7-8 (2011): 1760–64. http://dx.doi.org/10.1016/j.mcm.2010.11.087.

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da Silva, Nívea B., Marcos O. Prates, and Flávio B. Gonçalves. "Bayesian linear regression models with flexible error distributions." Journal of Statistical Computation and Simulation 90, no. 14 (2020): 2571–91. http://dx.doi.org/10.1080/00949655.2020.1783261.

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Shaw, J. E. H. "Numerical Bayesian Analysis of Some Flexible Regression Models." Statistician 36, no. 2/3 (1987): 147. http://dx.doi.org/10.2307/2348507.

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Dissertations / Theses on the topic "Flexible regression models"

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Mukherjee, Kathakali Ghosh. "Flexible regression models for functional neuroimaging." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7286/.

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Current practice for analysing functional neuroimaging data is to average the brain signals recorded at multiple sensors or channels on the scalp over time across hundreds of trials or replicates to eliminate noise and enhance the underlying signal of interest. These studies recording brain signals non-invasively using functional neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) generate complex, high dimensional and noisy data for many subjects at a number of replicates. Single replicate (or single trial) analysis of neuroimaging data have gained fo
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Roemmele, Eric S. "A Flexible Zero-Inflated Poisson Regression Model." UKnowledge, 2019. https://uknowledge.uky.edu/statistics_etds/38.

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A practical problem often encountered with observed count data is the presence of excess zeros. Zero-inflation in count data can easily be handled by zero-inflated models, which is a two-component mixture of a point mass at zero and a discrete distribution for the count data. In the presence of predictors, zero-inflated Poisson (ZIP) regression models are, perhaps, the most commonly used. However, the fully parametric ZIP regression model could sometimes be restrictive, especially with respect to the mixing proportions. Taking inspiration from some of the recent literature on semiparametric mi
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Lynch, James Charles. "A flexible class of models for regression modelling of multivariate failure time data /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/9561.

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Fischer, Manfred M. "Neural networks. A class of flexible non-linear models for regression and classification." Elgar, 2015. http://epub.wu.ac.at/4763/1/NN%2DHandbook%2Dchapter_Fischer_20120809.pdf.

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BERNASCONI, DAVIDE PAOLO. "Dynamic prediction in survival analysis with binary non-reversible time-dependent treatment indicator." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/76772.

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Negli studi clinici spesso è di interesse confrontare la sopravvivenza di pazienti appartenenti a due o più gruppi di trattamento. In alcune situazioni, la classificazione non è effettuata all’inizio del follow-up ma cambia nel tempo. Ad esempio, tutti i pazienti sono sottoposti ad un trattamento iniziale ed alcuni lo continuano mentre altri cambiano dopo un certo periodo di tempo. In questo caso il trattamento è rappresentato da una variabile binaria tempo-dipendente. Un contesto tipico è il confronto tra chemioterapia e trapianto di cellule staminali nella Leucemia Linfoblastica Acuta. In qu
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Luo, Zairen. "Flexible Pavement Condition Model Using Clusterwise Regression and Mechanistic-Empirical Procedure for Fatigue Cracking Modeling." See Full Text at OhioLINK ETD Center (Requires Adobe Acrobat Reader for viewing), 2005. http://www.ohiolink.edu/etd/view.cgi?toledo1133560069.

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Dissertation (Ph.D.)--University of Toledo, 2005.<br>Typescript. "A dissertation [submitted] as partial fulfillment of the requirements of the Doctor of Philosophy degree in Engineering." Bibliography: leaves 90-99.
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Hossain, Shahadut. "Dealing with measurement error in covariates with special reference to logistic regression model: a flexible parametric approach." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/408.

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In many fields of statistical application the fundamental task is to quantify the association between some explanatory variables or covariates and a response or outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely we measure the relevant covariates. In many instances, we can not measure some of the covariates accurately, rather we can measure noisy versions of them. In statistical terminology this is known as measurement errors or errors in variables. Regression analyses based on noisy covariate measurements lead to biased and inacc
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Verssani, Bruna Aparecida Wruck. "Modelo de regressão para sistemas reparáveis: um estudo da confiabilidade de colhedoras de cana-de-açúcar." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-22012019-173525/.

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A análise de confiabilidade desempenha um papel fundamental para estudos de durabilidade e otimização de tempos de reparo em sistemas reparáveis. Equipamentos como colhedoras de cana-de-açúcar que após a falha e um reparo voltam a exercer sua função objetivo são classificados como sistemas reparáveis. O objetivo deste trabalho consistiu em propor alternativas de modelagem para sistemas complexos, que apresentam grande variabilidade no comportamento da função intensidade de falha. Foi proposta a nova distribuição odd log-logística Weibull flexível generalizada (GOLLFW) e um modelo de regressão
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Tran, Xuan Quang. "Les modèles de régression dynamique et leurs applications en analyse de survie et fiabilité." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0147/document.

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Cette thèse a été conçu pour explorer les modèles dynamiques de régression, d’évaluer les inférences statistiques pour l’analyse des données de survie et de fiabilité. Ces modèles de régression dynamiques que nous avons considérés, y compris le modèle des hasards proportionnels paramétriques et celui de la vie accélérée avec les variables qui peut-être dépendent du temps. Nous avons discuté des problèmes suivants dans cette thèse.Nous avons présenté tout d’abord une statistique de test du chi-deux généraliséeY2nquiest adaptative pour les données de survie et fiabilité en présence de trois cas,
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Mackenzie, Monique L. "Flexible Mixed Models: Regression Splines and Thin-Plate Regression Splines in a Mixed Model Framework." 2005. http://hdl.handle.net/2292/650.

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Whole document restricted, see Access Instructions file below for details of how to access the print copy.<br>Regression splines and thin-plate regression splines were fitted inside generalized linear mixed models with good results. Their role in prediction and as exploratory tools are examined. Regression splines were specified in advance using biological information and compared with knot positions chosen using the data available. A forwards selection procedure was used to choose knots for thin-plate regression splines, and both cross-validation and fit statistics were used to discriminate b
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Books on the topic "Flexible regression models"

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Center, Ames Research, ed. On the reliable and flexible solution of practical subset regression problems. National Aeronautics and Space Administration, Ames Research Center, 1987.

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Park, Hyung. Flexible Regression Models for Estimating Interactions between a Treatment and Scalar/Functional Predictors. [publisher not identified], 2018.

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Heller, Gillian Z., Vlasios Voudouris, Mikis D. Stasinopoulos, Robert A. Rigby, and Fernanda de Bastiani. Flexible Regression and Smoothing. Taylor & Francis Group, 2020.

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Dunson, David. Flexible Bayes regression of epidemiologic data. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.1.

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This article focuses on flexible Bayes regression of epidemiologic data involving pregnancy outcomes. It first provides an overview of finite mixture models and nonparametric Bayes methods before discussing some of the possibilities focusing on gestational age at delivery, DDE and age data from the Longnecker et al. (2001) study. More specifically, it examines how risk of premature delivery is impacted by maternal exposure to the pesticide DDT. The results showcase the use of Bayesian analysis in epidemiological studies that collect continuous health outcomes data, and in which the scientific
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Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Heller, Gillian Z., Vlasios Voudouris, Mikis D. Stasinopoulos, Robert A. Rigby, and Fernanda De Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Heller, Gillian Z., Vlasios Voudouris, Mikis D. Stasinopoulos, Robert A. Rigby, and Fernanda De Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Heller, Gillian Z., Vlasios Voudouris, Mikis D. Stasinopoulos, Robert A. Rigby, and Fernanda De Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Book chapters on the topic "Flexible regression models"

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Au, Charles, and S. T. Boris Choy. "An Application of Bayesian Seemingly Unrelated Regression Models with Flexible Tails." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54084-9_11.

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Radwan, Mostafa M., Mostafa A. Abo-Hashema, Hamdy P. Faheem, and Mostafa D. Hashem. "ANN-Based Fatigue and Rutting Prediction Models Versus Regression-Based Models for Flexible Pavements." In Recent Developments in Pavement Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34196-1_9.

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Titterington, D. M. "Optimal Design in Flexible Models, Including Feed-Forward Networks and Nonparametric Regression." In Nonconvex Optimization and Its Applications. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3419-5_23.

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Mantovan, Pietro, and Andrea Pastore. "Flexible Dynamic Regression Models for Real-time Forecasting of Air Pollutant Concentration." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17111-6_22.

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Migliorati, Sonia, Agnese M. Di Brisco, and Andrea Ongaro. "The Flexible Beta Regression Model." In Data Analysis and Applications 1. John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119597568.ch3.

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Kriksciuniene, Dalia, Virgilijus Sakalauskas, Ivana Ognjanović, and Ramo Šendelj. "Discovering Healthcare Data Patterns by Artificial Intelligence Methods." In Intelligent Systems for Sustainable Person-Centered Healthcare. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79353-1_10.

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AbstractThe variety of the artificial intelligence and machine learning methods are applied for data analysis in various areas, including the data-rich healthcare domain. However, aiming to improve health care efficiency and use the captured information to improve treatment methods is often hampered by poor quality of medical data collections, as high percent of health data are unstructured and preserved in different systems and formats. In addition, it is not always agreed which methods of artificial intelligence and machine learning perform better in different problem areas, and which comput
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Jung, Yu-Jin, and Yong-Ik Yoon. "Flexible Multi-level Regression Model for Prediction of Pedestrian Abnormal Behavior." In Advances in Parallel and Distributed Computing and Ubiquitous Services. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0068-3_17.

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Antosz, Katarzyna. "Prediction Model of Product Quality in Production Company: Based on PCA and Logistic Regression." In Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38165-2_50.

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Miao, Yinsen, Jeong Hwan Kook, Yadong Lu, Michele Guindani, and Marina Vannucci. "Scalable Bayesian variable selection regression models for count data." In Flexible Bayesian Regression Modelling. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-815862-3.00015-9.

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Rahman, Mohammad Arshad, and Shubham Karnawat. "Flexible Bayesian Quantile Regression in Ordinal Models." In Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B. Emerald Publishing Limited, 2019. http://dx.doi.org/10.1108/s0731-90532019000040b011.

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Conference papers on the topic "Flexible regression models"

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Ryseck, Peter, Racheal Erhard, Michael Cunningham, Feyyaz Guener, Monica Londono, and Zouhair Mahboubi. "Gaussian Process Surrogate Model for eVTOL Propeller Aerodynamics." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-397.

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Gaussian Process Regression (GPR) is a flexible, non-parametric machine learning method well-suited for regression tasks. In the context of modeling aerodynamic propellers, GPR significantly reduces the amount of computationally expensive training data needed compared to simpler interpolation or curve-fitting approaches for the same level of accuracy. This work explores several strategies for building a surrogate model of an isolated propeller for the Joby Aviation tilt-propeller electric vertical take-off and landing (eVTOL) aircraft. To better capture sharp local variations in output quantit
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Božić, Dubravka, Biserka Runje, Andrej Razumić, Dragutin Lisjak, and Branko Strbac. "RISK ASSESSMENT FOR LINEAR REGRESSION MODELS IN METROLOGY: HYPOTHETICAL CASES." In XV INTERNATIONAL SCIENTIFIC CONFERENCE MMA 2024 – Flexible Technologies. Faculty of Technical Sciences, 2024. http://dx.doi.org/10.24867/mma-2024-03-010.

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Okuno, Alex, and Alberto Ferreira. "Generalized linear tree: a flexible algorithm for predicting continuous variables." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai2021072420.

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Tree-based models are popular among regression methods to predict continuous variables. Also, Generalized Linear Models (GLMs) are pretty standard in many statistical applications and provide a generalization to many of the most commonly applied statistical procedures. However, in most regression tree methods, there is only one theoretical model associated for prediction in the final nodes, like multiple linear regression, logistic regressions, polynomial models, Poisson models, among others. We, therefore, propose a new tree method in which we estimate a GLM in each leaf node of the estimated
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Usta, I. "Robust regression models based on flexible maximum entropy distributions." In International Conference on Quality, Reliability, Risk, Maintenance and Safety Engineering, edited by Y. M. Kantar. WIT Press, 2015. http://dx.doi.org/10.2495/qr2mse140421.

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Gupta, Ashish Kumar, Nivedita Naik, and Amol D. Rahulkar. "A Comparative Study of Regression Models for SoC Estimation in Electric Vehicle." In 2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE). IEEE, 2023. http://dx.doi.org/10.1109/icepe57949.2023.10201550.

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Mendes de Sousa, José Renato, and Anderson Cunha dos Santos. "Symbolic Regression Equations to Predict the Maximum Stress Concentration Factors in Flexible Pipes With Damaged Tensile Armors." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-122906.

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Abstract Flexible pipes are fundamental in various offshore oil and gas exploration applications, such as production, gas injection, gas lift, or water injection lines. However, such structures can be damaged infield, reducing their structural capacity and leading to costs associated with replacements, production loss, or oil leakage. Among the various failure modes of flexible pipes, the rupture of the tensile armors is critical, as these armors withstand the axial loads imposed on the pipe. In this context, this work aims to contribute to evaluating the structural integrity of damaged flexib
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Maseda, Tomé, Jonatan Enes, Roberto R. Expósito, and Juan Touriño. "CPUPowerWatcher/Seer: Automated and Flexible CPU Power Modelling." In VII Congreso XoveTIC: impulsando el talento científico. Servizo de Publicacións. Universidade da Coruña, 2024. https://doi.org/10.17979/spudc.9788497498913.44.

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Power supply is a key limitation when scaling supercomputing capabilities, making power consumption a major challenge in HPC field. To develop energy-efficient tools, it is essential to have an accurate power consumption modelling. Although previous works proposed several approaches to model CPU power, building models in an automated and adaptable way, and accurately predicting power, remains complex. This work presents two tools: CPUPowerWatcher, which gathers CPU metrics during the execution of user-defined workloads, and CPUPowerSeer, which build models to predict power from time series dat
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Gonzalez, Gabriel M., José Renato M. de Sousa, Luis V. S. Sagrilo, Ricardo R. Martins, and Djalene M. Rocha. "A Symbolic Regression Formulation to Estimate the Lateral Buckling Resistance of Tensile Armors in Flexible Pipes." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95510.

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Abstract In this work, a previously proposed finite element is applied in conjunction with a modal approach to predict the lateral buckling resistance of the tensile armors in flexible pipes. The finite element represents the mechanical behavior of tensile armors settled on elastic foundations, which model the frictional interaction between these armors and the surrounding layers. This FE modal approach is used to evaluate the buckling response of 44 different tensile armors considering 15 different friction coefficients between layers. The responses obtained formed a dataset employed in symbo
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Tsiligaridis, John. "Approaches of Classification Models for Sentiment Analysis." In 5th International Conference on Advanced Natural Language Processing. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141007.

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Sentiment analysis (SA) is a Natural Language Processing (NLP) method that helps identify the emotions in text. It is the automated process of identifying and classifying emotions in a text as positive, negative, or neutral sentiment. This way, companies can understand customers’ sentiments, improve their products and services accordingly, and determine effective strategies. The need to discover the algorithm with the best classification performance is obvious. To this end, two different approaches for Sentiment Analysis problems are presented. The first one is based on Machine Learning (ML) m
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Hatakeyama, Waku, Cong Wang, and Lu Lu. "Nonparametric Tool Path Compensation for Machining Flexible Parts." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9640.

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This paper discusses the compensation of tool paths for machining flexible parts. Despite various research published on the topic, machining in practice nowadays remains limited to tool path planning based on only the geometric models of the parts and tools. This is mainly because that tool path compensation methods usually require accurate physical information of the systems and rely on analytical or finite element simulations, which are often not available to the end-users. In regards to this problem, this paper presents data-oriented nonparametric learning methods that require solely the ge
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Reports on the topic "Flexible regression models"

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Cattaneo, Matias D., Richard K. Crump, Max H. Farrell, and Yingjie Feng. Nonlinear Binscatter Methods. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1110.

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Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to primarily construct flexible yet interpretable visualization methods, but they can also be used to estimate treatment effects, assess uncertainty, and test substantive domain-specific hypotheses. This paper introduces novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation methods, covering generalized linear, robust, and quantile regressio
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Lu, Tianjun, Jian-yu Ke, Fynnwin Prager, and Jose N. Martinez. “TELE-commuting” During the COVID-19 Pandemic and Beyond: Unveiling State-wide Patterns and Trends of Telecommuting in Relation to Transportation, Employment, Land Use, and Emissions in Calif. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2147.

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Telecommuting, the practice of working remotely at home, increased significantly (25% to 35%) early in the COVID-19 pandemic. This shift represented a major societal change that reshaped the family, work, and social lives of many Californians. These changes also raise important questions about what factors influenced telecommuting before, during, and after COVID-19, and to what extent changes in telecommuting have influenced transportation patterns across commute modes, employment, land use, and environment. The research team conducted state-level telecommuting surveys using a crowd-sourced pl
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the proj
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