Literatura académica sobre el tema "Data weighting function"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Data weighting function".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Data weighting function"
Dombi, József y Tamás Jónás. "Towards a general class of parametric probability weighting functions". Soft Computing 24, n.º 21 (24 de septiembre de 2020): 15967–77. http://dx.doi.org/10.1007/s00500-020-05335-3.
Texto completoYing Han, Pang, Andrew Teoh Beng Jin y 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.
Texto completoLu, Guangyin, Dongxing Zhang, Shujin Cao, Yihuai Deng, Gang Xu, Yihu Liu, Ziqiang Zhu y Peng Chen. "Spherical Planting Inversion of GRAIL Data". Applied Sciences 13, n.º 5 (6 de marzo de 2023): 3332. http://dx.doi.org/10.3390/app13053332.
Texto completoBedini, L., S. Fossi y R. Reggiannini. "Generalised crosscorrelator with data-estimated weighting function: a simulation analysis". IEE Proceedings F Communications, Radar and Signal Processing 133, n.º 2 (1986): 195. http://dx.doi.org/10.1049/ip-f-1.1986.0030.
Texto completoKELLER, ANNETTE y FRANK KLAWONN. "FUZZY CLUSTERING WITH WEIGHTING OF DATA VARIABLES". International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, n.º 06 (diciembre de 2000): 735–46. http://dx.doi.org/10.1142/s0218488500000538.
Texto completoBedini, L., S. Fossi y R. Reggiannini. "Erratum: Generalised crosscorrelator with data-estimated weighting function: a simulation analysis". IEE Proceedings F Communications, Radar and Signal Processing 133, n.º 3 (1986): 231. http://dx.doi.org/10.1049/ip-f-1.1986.0039.
Texto completoJiang, Ray, Shangtong Zhang, Veronica Chelu, Adam White y Hado van Hasselt. "Learning Expected Emphatic Traces for Deep RL". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 6 (28 de junio de 2022): 7015–23. http://dx.doi.org/10.1609/aaai.v36i6.20660.
Texto completoBlahak, 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, n.º 7 (1 de julio de 2008): 1182–96. http://dx.doi.org/10.1175/2007jtecha1010.1.
Texto completoNie, Lichao, Zhao Ma, Bin Liu, Zhenhao Xu, Wei Zhou, Chengkun Wang, Junyang Shao y Xin Yin. "A Weighting Function-Based Method for Resistivity Inversion in Subsurface Investigations". Journal of Environmental and Engineering Geophysics 25, n.º 1 (marzo de 2020): 129–38. http://dx.doi.org/10.2113/jeeg19-029.
Texto completoVitale, Andrea y Maurizio Fedi. "Self-constrained inversion of potential fields through a 3D depth weighting". GEOPHYSICS 85, n.º 6 (1 de noviembre de 2020): G143—G156. http://dx.doi.org/10.1190/geo2019-0812.1.
Texto completoTesis sobre el tema "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.
Texto completoDI, 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.
Texto completoJohnson, 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.
Texto completoMoreno, Betancur Margarita. "Regression modeling with missing outcomes : competing risks and longitudinal data". Thesis, Paris 11, 2013. http://www.theses.fr/2013PA11T076/document.
Texto completoMissing 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.
Texto completoLibros sobre el tema "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.
Buscar texto completoChance, Kelly y Randall V. Martin. Data Fitting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199662104.003.0011.
Texto completoCapítulos de libros sobre el tema "Data weighting function"
Yamanaka, Masao. "Effective Delayed Neutron Fraction". En 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.
Texto completoBlackledge, J. M., M. A. Fiddy y W. A. Ward. "Resolution Enhancement of Processed Seismic Data Using Prior Weighting Functions". En Acoustical Imaging, 207–19. Boston, MA: Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2523-9_20.
Texto completoPaquet, Hugo. "Bayesian strategies: probabilistic programs as generalised graphical models". En Programming Languages and Systems, 519–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72019-3_19.
Texto completoShoji, Isao. "Nonparametric Estimation of Nonlinear Dynamics by Local Linear Approximation". En Chaos and Complexity Theory for Management, 368–79. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2509-9.ch019.
Texto completoKennedy, Georgina, Mark Dras y Blanca Gallego. "Augmentation of Electronic Medical Record Data for Deep Learning". En MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220144.
Texto completoSong, Dongran, Ziqun Li, Jian Yang, Mi Dong, Xiaojiao Chen y Liansheng Huang. "Nonlinear Intelligent Predictive Control for the Yaw System of Large-Scale Wind Turbines". En Nonlinear Systems - Recent Developments and Advances [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.105484.
Texto completoDi Cera, Enrico. "[4] Use of weighting functions in data fitting". En Methods in Enzymology, 68–87. Elsevier, 1992. http://dx.doi.org/10.1016/0076-6879(92)10006-y.
Texto completoCarnero, María Carmen y Javier Cárcel-Carrasco. "Effects of Recession on Asset Management Performance in Small Businesses in Spain". En Cases on Optimizing the Asset Management Process, 325–53. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7943-5.ch013.
Texto completoBüyük, Ersin. "Pareto-Based Multiobjective Particle Swarm Optimization: Examples in Geophysical Modeling". En Swarm Intelligence [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97067.
Texto completoActas de conferencias sobre el tema "Data weighting function"
Galbraith, Mike, Zhengsheng Yao y Randy Kolesar. "Seismic data interpolation with f‐p domain spectra weighting function". En SEG Technical Program Expanded Abstracts 2011. Society of Exploration Geophysicists, 2011. http://dx.doi.org/10.1190/1.3627838.
Texto completoMa, Rui y John B. Ferris. "Terrain Gridding Using a Stochastic Weighting Function". En 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.
Texto completoCella, F. y M. Fedi. "Inversion of Potential Field Data Using the Structural Index as Weighting Function Rate Decay". En 70th EAGE Conference and Exhibition - Workshops and Fieldtrips. European Association of Geoscientists & Engineers, 2008. http://dx.doi.org/10.3997/2214-4609.20147784.
Texto completoLiu, Zexin, Heather T. Ma y Fei Chen. "A new data-driven band-weighting function for predicting the intelligibility of noise-suppressed speech". En 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.
Texto completoFernandez, Charles, Arun Kr Dev, Rose Norman, Wai Lok Woo y 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". En 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.
Texto completoKurzawski, Andrew y Ofodike A. Ezekoye. "Inversion for Fire Heat Release Rate Using Transient Heat Flux Data". En ASME 2017 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ht2017-5107.
Texto completoIsrar, Ali, Hong Z. Tan, James Mynderse y George T. C. Chiu. "A Psychophysical Model of Motorcycle Handlebar Vibrations". En ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41504.
Texto completoRamanujan, Devarajan, William Z. Bernstein, Fu Zhao y Karthik Ramani. "Addressing Uncertainties Within Product Redesign for Sustainability: A Function Based Framework". En ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47137.
Texto completoCavalcante, Everton, Thais Batista, Marcel Oliveira, Jorge Pereira, Victor Ribeiro y Matthieu Oliveira. "A Multidimensional Approach for Logistics Routing in the Smart Territory". En Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2022. http://dx.doi.org/10.5753/sbsi_estendido.2022.222988.
Texto completoKim, Hong-Min y Kwang-Yong Kim. "Optimization of Three-Dimensional Angled Ribs With RANS Analysis of Turbulent Heat Transfer". En ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53346.
Texto completo