Littérature scientifique sur le sujet « Data weighting function »
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Articles de revues sur le sujet "Data weighting function"
Dombi, József, et Tamás Jónás. « Towards a general class of parametric probability weighting functions ». Soft Computing 24, no 21 (24 septembre 2020) : 15967–77. http://dx.doi.org/10.1007/s00500-020-05335-3.
Texte intégralYing Han, Pang, Andrew Teoh Beng Jin et Lim Heng Siong. « Eigenvector Weighting Function in Face Recognition ». Discrete Dynamics in Nature and Society 2011 (2011) : 1–15. http://dx.doi.org/10.1155/2011/521935.
Texte intégralLu, Guangyin, Dongxing Zhang, Shujin Cao, Yihuai Deng, Gang Xu, Yihu Liu, Ziqiang Zhu et Peng Chen. « Spherical Planting Inversion of GRAIL Data ». Applied Sciences 13, no 5 (6 mars 2023) : 3332. http://dx.doi.org/10.3390/app13053332.
Texte intégralBedini, L., S. Fossi et R. Reggiannini. « Generalised crosscorrelator with data-estimated weighting function : a simulation analysis ». IEE Proceedings F Communications, Radar and Signal Processing 133, no 2 (1986) : 195. http://dx.doi.org/10.1049/ip-f-1.1986.0030.
Texte intégralKELLER, ANNETTE, et FRANK KLAWONN. « FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES ». International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no 06 (décembre 2000) : 735–46. http://dx.doi.org/10.1142/s0218488500000538.
Texte intégralBedini, L., S. Fossi et R. Reggiannini. « Erratum : Generalised crosscorrelator with data-estimated weighting function : a simulation analysis ». IEE Proceedings F Communications, Radar and Signal Processing 133, no 3 (1986) : 231. http://dx.doi.org/10.1049/ip-f-1.1986.0039.
Texte intégralJiang, Ray, Shangtong Zhang, Veronica Chelu, Adam White et Hado van Hasselt. « Learning Expected Emphatic Traces for Deep RL ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 6 (28 juin 2022) : 7015–23. http://dx.doi.org/10.1609/aaai.v36i6.20660.
Texte intégralBlahak, Ulrich. « An Approximation to the Effective Beam Weighting Function for Scanning Meteorological Radars with an Axisymmetric Antenna Pattern ». Journal of Atmospheric and Oceanic Technology 25, no 7 (1 juillet 2008) : 1182–96. http://dx.doi.org/10.1175/2007jtecha1010.1.
Texte intégralNie, Lichao, Zhao Ma, Bin Liu, Zhenhao Xu, Wei Zhou, Chengkun Wang, Junyang Shao et Xin Yin. « A Weighting Function-Based Method for Resistivity Inversion in Subsurface Investigations ». Journal of Environmental and Engineering Geophysics 25, no 1 (mars 2020) : 129–38. http://dx.doi.org/10.2113/jeeg19-029.
Texte intégralVitale, Andrea, et Maurizio Fedi. « Self-constrained inversion of potential fields through a 3D depth weighting ». GEOPHYSICS 85, no 6 (1 novembre 2020) : G143—G156. http://dx.doi.org/10.1190/geo2019-0812.1.
Texte intégralThèses sur le sujet "Data weighting function"
Sarmah, Dipsikha. « Evaluation of Spatial Interpolation Techniques Built in the Geostatistical Analyst Using Indoor Radon Data for Ohio,USA ». University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1350048688.
Texte intégralDI, CORSO EVELINA. « Text miner's little helper : scalable self-tuning methodologies for knowledge exploration ». Doctoral thesis, Politecnico di Torino, 2019. http://hdl.handle.net/11583/2738395.
Texte intégralJohnson, Gregory K. « The Optimal Weighting of Pre-Election Polling Data ». Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2378.pdf.
Texte intégralMoreno, Betancur Margarita. « Regression modeling with missing outcomes : competing risks and longitudinal data ». Thesis, Paris 11, 2013. http://www.theses.fr/2013PA11T076/document.
Texte intégralMissing data are a common occurrence in medical studies. In regression modeling, missing outcomes limit our capability to draw inferences about the covariate effects of medical interest, which are those describing the distribution of the entire set of planned outcomes. In addition to losing precision, the validity of any method used to draw inferences from the observed data will require that some assumption about the mechanism leading to missing outcomes holds. Rubin (1976, Biometrika, 63:581-592) called the missingness mechanism MAR (for “missing at random”) if the probability of an outcome being missing does not depend on missing outcomes when conditioning on the observed data, and MNAR (for “missing not at random”) otherwise. This distinction has important implications regarding the modeling requirements to draw valid inferences from the available data, but generally it is not possible to assess from these data whether the missingness mechanism is MAR or MNAR. Hence, sensitivity analyses should be routinely performed to assess the robustness of inferences to assumptions about the missingness mechanism. In the field of incomplete multivariate data, in which the outcomes are gathered in a vector for which some components may be missing, MAR methods are widely available and increasingly used, and several MNAR modeling strategies have also been proposed. On the other hand, although some sensitivity analysis methodology has been developed, this is still an active area of research. The first aim of this dissertation was to develop a sensitivity analysis approach for continuous longitudinal data with drop-outs, that is, continuous outcomes that are ordered in time and completely observed for each individual up to a certain time-point, at which the individual drops-out so that all the subsequent outcomes are missing. The proposed approach consists in assessing the inferences obtained across a family of MNAR pattern-mixture models indexed by a so-called sensitivity parameter that quantifies the departure from MAR. The approach was prompted by a randomized clinical trial investigating the benefits of a treatment for sleep-maintenance insomnia, from which 22% of the individuals had dropped-out before the study end. The second aim was to build on the existing theory for incomplete multivariate data to develop methods for competing risks data with missing causes of failure. The competing risks model is an extension of the standard survival analysis model in which failures from different causes are distinguished. Strategies for modeling competing risks functionals, such as the cause-specific hazards (CSH) and the cumulative incidence function (CIF), generally assume that the cause of failure is known for all patients, but this is not always the case. Some methods for regression with missing causes under the MAR assumption have already been proposed, especially for semi-parametric modeling of the CSH. But other useful models have received little attention, and MNAR modeling and sensitivity analysis approaches have never been considered in this setting. We propose a general framework for semi-parametric regression modeling of the CIF under MAR using inverse probability weighting and multiple imputation ideas. Also under MAR, we propose a direct likelihood approach for parametric regression modeling of the CSH and the CIF. Furthermore, we consider MNAR pattern-mixture models in the context of sensitivity analyses. In the competing risks literature, a starting point for methodological developments for handling missing causes was a stage II breast cancer randomized clinical trial in which 23% of the deceased women had missing cause of death. We use these data to illustrate the practical value of the proposed approaches
Yoshinaga, Kenji. « Comparison of phase synchronization measures for identifying stimulus- induced functional connectivity in human magnetoencephalographic and simulated data ». Kyoto University, 2020. http://hdl.handle.net/2433/259724.
Texte intégralLivres sur le sujet "Data weighting function"
Lerch, F. J. Optimum data weighting and error calibration for estimation of gravitational parameters. Greenbelt, Md : National Aeronautics and Space Administration, Goddard Space Flight Center, 1989.
Trouver le texte intégralChance, Kelly, et Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.
Texte intégralChapitres de livres sur le sujet "Data weighting function"
Yamanaka, Masao. « Effective Delayed Neutron Fraction ». Dans Accelerator-Driven System at Kyoto University Critical Assembly, 83–123. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0344-0_4.
Texte intégralBlackledge, J. M., M. A. Fiddy et W. A. Ward. « Resolution Enhancement of Processed Seismic Data Using Prior Weighting Functions ». Dans Acoustical Imaging, 207–19. Boston, MA : Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2523-9_20.
Texte intégralPaquet, Hugo. « Bayesian strategies : probabilistic programs as generalised graphical models ». Dans Programming Languages and Systems, 519–47. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_19.
Texte intégralShoji, Isao. « Nonparametric Estimation of Nonlinear Dynamics by Local Linear Approximation ». Dans Chaos and Complexity Theory for Management, 368–79. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2509-9.ch019.
Texte intégralKennedy, Georgina, Mark Dras et Blanca Gallego. « Augmentation of Electronic Medical Record Data for Deep Learning ». Dans MEDINFO 2021 : One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220144.
Texte intégralSong, Dongran, Ziqun Li, Jian Yang, Mi Dong, Xiaojiao Chen et Liansheng Huang. « Nonlinear Intelligent Predictive Control for the Yaw System of Large-Scale Wind Turbines ». Dans Nonlinear Systems - Recent Developments and Advances [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105484.
Texte intégralDi Cera, Enrico. « [4] Use of weighting functions in data fitting ». Dans Methods in Enzymology, 68–87. Elsevier, 1992. http://dx.doi.org/10.1016/0076-6879(92)10006-y.
Texte intégralCarnero, María Carmen, et Javier Cárcel-Carrasco. « Effects of Recession on Asset Management Performance in Small Businesses in Spain ». Dans Cases on Optimizing the Asset Management Process, 325–53. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7943-5.ch013.
Texte intégralBüyük, Ersin. « Pareto-Based Multiobjective Particle Swarm Optimization : Examples in Geophysical Modeling ». Dans Swarm Intelligence [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97067.
Texte intégralActes de conférences sur le sujet "Data weighting function"
Galbraith, Mike, Zhengsheng Yao et Randy Kolesar. « Seismic data interpolation with f‐p domain spectra weighting function ». Dans SEG Technical Program Expanded Abstracts 2011. Society of Exploration Geophysicists, 2011. http://dx.doi.org/10.1190/1.3627838.
Texte intégralMa, Rui, et John B. Ferris. « Terrain Gridding Using a Stochastic Weighting Function ». Dans ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6085.
Texte intégralCella, F., et M. Fedi. « Inversion of Potential Field Data Using the Structural Index as Weighting Function Rate Decay ». Dans 70th EAGE Conference and Exhibition - Workshops and Fieldtrips. European Association of Geoscientists & Engineers, 2008. http://dx.doi.org/10.3997/2214-4609.20147784.
Texte intégralLiu, Zexin, Heather T. Ma et Fei Chen. « A new data-driven band-weighting function for predicting the intelligibility of noise-suppressed speech ». Dans 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282082.
Texte intégralFernandez, Charles, Arun Kr Dev, Rose Norman, Wai Lok Woo et Shashi Bhushan Kumar. « Dynamic Positioning System : Systematic Weight Assignment for DP Sub-Systems Using Multi-Criteria Evaluation Technique Analytic Hierarchy Process and Validation Using DP-RI Tool With Deep Learning Algorithm ». Dans ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95485.
Texte intégralKurzawski, Andrew, et Ofodike A. Ezekoye. « Inversion for Fire Heat Release Rate Using Transient Heat Flux Data ». Dans ASME 2017 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ht2017-5107.
Texte intégralIsrar, Ali, Hong Z. Tan, James Mynderse et George T. C. Chiu. « A Psychophysical Model of Motorcycle Handlebar Vibrations ». Dans ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41504.
Texte intégralRamanujan, Devarajan, William Z. Bernstein, Fu Zhao et Karthik Ramani. « Addressing Uncertainties Within Product Redesign for Sustainability : A Function Based Framework ». Dans ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47137.
Texte intégralCavalcante, Everton, Thais Batista, Marcel Oliveira, Jorge Pereira, Victor Ribeiro et Matthieu Oliveira. « A Multidimensional Approach for Logistics Routing in the Smart Territory ». Dans Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2022. http://dx.doi.org/10.5753/sbsi_estendido.2022.222988.
Texte intégralKim, Hong-Min, et Kwang-Yong Kim. « Optimization of Three-Dimensional Angled Ribs With RANS Analysis of Turbulent Heat Transfer ». Dans ASME Turbo Expo 2004 : Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53346.
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