Bibliografías temáticas / Kernel estimates

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Literatura académica sobre el tema "Kernel estimates"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 13 de febrero de 2022

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Artículos de revistas sobre el tema "Kernel estimates"

1

Jones, M. C. "VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES." Australian Journal of Statistics 32, no. 3 (1990): 361–71. http://dx.doi.org/10.1111/j.1467-842x.1990.tb01031.x.

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Qian, Zhongmin. "Gradient estimates and heat kernel estimates." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 125, no. 5 (1995): 975–90. http://dx.doi.org/10.1017/s0308210500022599.

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In the first part of this paper, Yau's estimates for positive L-harmonic functions and Li and Yau's gradient estimates for the positive solutions of a general parabolic heat equation on a complete Riemannian manifold are obtained by the use of Bakry and Emery's theory. In the second part we establish a heat kernel bound for a second-order differential operator which has a bounded and measurable drift, using Girsanov's formula.
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Wang, Jiaping. "Global heat kernel estimates." Pacific Journal of Mathematics 178, no. 2 (1997): 377–98. http://dx.doi.org/10.2140/pjm.1997.178.377.

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Van Kerm, Philippe. "Adaptive Kernel Density Estimation." Stata Journal: Promoting communications on statistics and Stata 3, no. 2 (2003): 148–56. http://dx.doi.org/10.1177/1536867x0300300204.

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This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an “adaptive kernel” approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated density functions.
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Staniswalis, Joan G., and Vanessa Cooper. "Kernel Estimates of Dose Response." Biometrics 44, no. 4 (1988): 1103. http://dx.doi.org/10.2307/2531739.

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Brown, Philip J., and Peter W. K. Rundell. "Kernel Estimates for Categorical Data." Technometrics 27, no. 3 (1985): 293–99. http://dx.doi.org/10.1080/00401706.1985.10488054.

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Ghosh, Anil K., Probal Chaudhuri, and Debasis Sengupta. "Classification Using Kernel Density Estimates." Technometrics 48, no. 1 (2006): 120–32. http://dx.doi.org/10.1198/004017005000000391.

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Franke, J., and W. Hardle. "On Bootstrapping Kernel Spectral Estimates." Annals of Statistics 20, no. 1 (1992): 121–45. http://dx.doi.org/10.1214/aos/1176348515.

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SAMIUDDIN, M., and G. M. EL-SAYYAD. "On nonparametric kernel density estimates." Biometrika 77, no. 4 (1990): 865–74. http://dx.doi.org/10.1093/biomet/77.4.865.

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Metafune, G., D. Pallara, and A. Rhandi. "Kernel estimates for Schrödinger operators." Journal of Evolution Equations 6, no. 3 (2006): 433–57. http://dx.doi.org/10.1007/s00028-006-0259-6.

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Tesis sobre el tema "Kernel estimates"

1

Paulat, Martin [Verfasser]. "Sub-Riemannian geometry and heat kernel estimates / Martin Paulat." Kiel : Universitätsbibliothek Kiel, 2008. http://d-nb.info/1019659203/34.

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Rose, Christian. "Heat kernel estimates based on Ricci curvature integral bounds." Doctoral thesis, Universitätsbibliothek Chemnitz, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-228681.

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Any Riemannian manifold possesses a minimal solution of the heat equation for the Dirichlet Laplacian, called the heat kernel. During the last decades many authors investigated geometric properties of the manifold such that its heat kernel fulfills a so-called Gaussian upper bound. Especially compact and non-compact manifolds with lower bounded Ricci curvature have been examined and provide such Gaussian estimates. In the compact case it ended even with integral Ricci curvature assumptions. The important techniques to obtain Gaussian bounds are the symmetrization procedure for compact manifold
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Kühn, Franziska. "Probability and Heat Kernel Estimates for Lévy(-Type) Processes." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214839.

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In this thesis, we present a new existence result for Lévy-type processes. Lévy-type processes behave locally like a Lévy process, but the Lévy triplet may depend on the current position of the process. They can be characterized by their so-called symbol; this is the analogue of the characteristic exponent in the Lévy case. Using a parametrix construction, we prove the existence of Lévy-type processes with a given symbol under weak regularity assumptions on the regularity of the symbol. Applications range from existence results for stable-like processes and mixed processes to uniqueness result
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Nakamura, Chikara. "Asymptotic behaviors of random walks; application of heat kernel estimates." Kyoto University, 2018. http://hdl.handle.net/2433/232222.

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Orgoník, Svetoslav. "Stochastické modelování datových souborů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229729.

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Master's thesis is focused on implementing modern statistical methods for fitting propability distribution using kernel estimates with regard to the possibilities of their implementation on the PC and the application of specic data sets. Master's thesis is a part of project from MSMT of the Czech Republic no. 1M06047 Center for Quality and Reliability of Production.
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Weber, Andreas. "Heat kernel estimates and Lp spectral theory of locally symmetric spaces." Karlsruhe : Univ.-Verl. Karlsruhe, 2006. http://www.uvka.de/univerlag/volltexte/2007/198/.

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Claire, Narinder Singh. "Spectral theory and heat kernel estimates for higher order differential operators." Thesis, King's College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395811.

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Weber, Andreas [Verfasser]. "Heat kernel estimates and Lp spectral theory of locally symmetric spaces / von Andreas Weber." Karlsruhe : Univ.-Verl. Karlsruhe, 2007. http://d-nb.info/983600430/34.

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Kornienko, Olga [Verfasser], and Daniel [Akademischer Betreuer] Durstewitz. "Neural Representations and Decoding with Optimized Kernel Density Estimates / Olga Kornienko ; Betreuer: Daniel Durstewitz." Heidelberg : Universitätsbibliothek Heidelberg, 2015. http://d-nb.info/1180608836/34.

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Chayet, Maurice 1953. "Some general estimates for the heat kernel on symmetric spaces and related problems of integral Geometry." Thesis, McGill University, 1990. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74536.

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We use a method of analytic continuation introduced by M. Flensted-Jensen to study the asymptotic behaviour of the heat kernel on noncompact symmetric spaces, for values of the time parameter which are arbitrarily small or arbitrarily large. The same method is applied to one case of the inversion problem for the Abel transform. The results are illustrated with explicit computations for SL(3, R).
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Libros sobre el tema "Kernel estimates"

1

Resistance forms, quasisymmetric maps, and heat kernel estimates. American Mathematical Society, 2012.

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2

Weber, Andreas. Heat kernel estimates and L_1hnp spectral theory of locally symmetric spaces. Univ.-Verl. Karlsruhe, 2006.

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3

Volume doubling measures and heat kernel estimates on self-similar sets. American Mathematical Society, 2009.

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4

Barlow, M. T. Characterization of sub-Gaussian heat kernel estimates on strongly recurrent graphs. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2004.

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Kumagai, Takashi. Heat kernel estimates and parabolic Harnack imequalities on graphs and resistance forms. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2003.

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Fujii, Ichiro. Heat kernel estimates on the incipient infinite cluster for critical branching processes. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2007.

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Chen, Zhen-Qing. Heat Kernel Estimates for Jump Processes of Mixed Types on Metric Measure Spaces. Research Institute for Mathematical Sciences, Kyoto University, 2006.

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Barlow, M. T. Parabolic harnack inequality and heat kernel estimates for random walks with long range fumps. Kyōto Daigaku Sūri Kaiseki Kenkyūjo, 2007.

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9

Jenkins, Stephen P. Did the middle class shrink during the 1980's?: UK evidence from kernel density estimates. ESRC Research Centre on Micro-social Change, 1995.

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Newey, Whitney K. Kernel estimation of partial means and a general variance estimator. Dept. of Economics, Massachusetts Institute of Technology, 1992.

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Capítulos de libros sobre el tema "Kernel estimates"

1

Gazzola, Filippo, Hans-Christoph Grunau, and Guido Sweers. "Kernel Estimates." In Lecture Notes in Mathematics. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12245-3_4.

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Devroye, Luc, and Gábor Lugosi. "Multiparameter Kernel Estimates." In Combinatorial Methods in Density Estimation. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0125-7_12.

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Devroye, Luc, and Gábor Lugosi. "Bandwidth Selection for Kernel Estimates." In Combinatorial Methods in Density Estimation. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0125-7_11.

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Kumagai, Takashi. "Heat Kernel Estimates: General Theory." In Lecture Notes in Mathematics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03152-1_3.

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Phillips, Jeff M., and Wai Ming Tai. "Improved Coresets for Kernel Density Estimates." In Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975031.173.

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Kumagai, Takashi. "Heat Kernel Estimates Using Effective Resistance." In Lecture Notes in Mathematics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03152-1_4.

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Mayo, Michael, and Sean Goltz. "Constructing Document Vectors Using Kernel Density Estimates." In Modeling Decisions for Artificial Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67422-3_16.

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Kumagai, Takashi. "Heat Kernel Estimates for Random Weighted Graphs." In Lecture Notes in Mathematics. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03152-1_5.

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Łukasik, Szymon. "Parallel Computing of Kernel Density Estimates with MPI." In Computational Science – ICCS 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72588-6_120.

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Kunze, Markus, Luca Lorenzi, and Abdelaziz Rhandi. "Kernel Estimates for Nonautonomous Kolmogorov Equations with Potential Term." In Springer INdAM Series. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11406-4_12.

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Actas de conferencias sobre el tema "Kernel estimates"

1

Zhang, Xiao, and Shizhong Liao. "Online Kernel Selection via Incremental Sketched Kernel Alignment." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/433.

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In contrast to offline kernel selection, online kernel selection must rise to the new challenges of passing the training set once, selecting optimal kernels and updating hypotheses at each round, enjoying a sublinear regret bound for online kernel learning, and requiring a constant maintenance time complexity at each round and an efficient overall time complexity integrated with online kernel learning. However, most of existing online kernel selection approaches can not meet the new challenges. To address this issue, we propose a novel online kernel selection approach via the incremental sketc
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Broadwater, Joshua, Rama Chellappa, Amit Banerjee, and Philippe Burlina. "Kernel fully constrained least squares abundance estimates." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423736.

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Bas, Erhan, and Deniz Erdogmus. "Polytope kernel density estimates on Delaunay graphs." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946739.

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Schubert, Erich, Arthur Zimek, and Hans-Peter Kriegel. "Generalized Outlier Detection with Flexible Kernel Density Estimates." In Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.63.

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Zheng, Yan, Jeffrey Jestes, Jeff M. Phillips, and Feifei Li. "Quality and efficiency for kernel density estimates in large data." In the 2013 international conference. ACM Press, 2013. http://dx.doi.org/10.1145/2463676.2465319.

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Zheng, Yan, Yi Ou, Alexander Lex, and Jeff M. Phillips. "Visualization of Big Spatial Data using Coresets for Kernel Density Estimates." In 2017 IEEE Visualization in Data Science (VDS). IEEE, 2017. http://dx.doi.org/10.1109/vds.2017.8573446.

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Baum, Marcus, Patrick Ruoff, Dominik Itte, and Uwe D. Hanebeck. "Optimal point estimates for multi-target states based on kernel distances." In 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012. http://dx.doi.org/10.1109/cdc.2012.6426189.

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Enright, M. P., R. C. McClung, S. J. Hudak, and H. R. Millwater. "Application of Nonparametric Methods to Rainflow Stress Density Estimation of Gas Turbine Engine Usage." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-90780.

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The risk of fracture associated with high energy rotating components in aircraft gas turbine engines can be sensitive to small changes in applied stress values which are often difficult to measure and predict. Although a parametric approach is often used to characterize random variables, it is difficult to apply to multimodal densities. Nonparametric methods provide a direct fit to the data, and can be used to estimate the multimodal densities often associated with rainflow stress data. In this paper, a comparison of parametric and nonparametric methods is presented for density estimation of r
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Zheng, Yan, and Jeff M. Phillips. "L∞ Error and Bandwidth Selection for Kernel Density Estimates of Large Data." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. http://dx.doi.org/10.1145/2783258.2783357.

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Chen, Q., P. Goulding, D. Sandoz, and R. Wynne. "The application of kernel density estimates to condition monitoring for process industries." In Proceedings of the 1998 American Control Conference (ACC). IEEE, 1998. http://dx.doi.org/10.1109/acc.1998.703187.

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Informes sobre el tema "Kernel estimates"

1

Kusuoka, S., and D. Stroock. Long Time Estimates for the Heat Kernel Associated with a Uniformly Subellipic Symmetric Second Order Operator. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada169949.

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Poston, Wendy, George Rogers, Carey Priebe, and Jeffrey Solka. Resistive Grid Kernel Estimator (RGKE). Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada253520.

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Burke, Timothy P., Brian C. Kiedrowski, and William R. Martin. Flux and Reaction Rate Kernel Density Estimators in OpenMC. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1090692.

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Lio, Y. L., and W. J. Padgett. Some Convergence Results for Kernel-Type Quantile Estimators under Censoring. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada162837.

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Kasparian, Azniv. Compressed Product of Balls and Lower Boundary Estimates on Bergman Kernels. GIQ, 2012. http://dx.doi.org/10.7546/giq-4-2003-193-205.

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Zhao, L. C. Exponential Bounds of Mean Error for the Kernal Estimates of Regression Functions. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada167345.

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Burke, Timothy P., Brian C. Kiedrowski, and William R. Martin. Flux and Reaction Rate Kernel Density Estimators in OpenMC with Physics-Based Bandwidths. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1090695.

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Hall, P., and J. S. Marron. The Amount of Noise Inherent in Bandwidth Selection for a Kernel Density Estimator. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160235.

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Lio, Y. O., W. J. Padgett, and K. F. Yu. On the Asymptotic Properties of a Kernel-Type Quantile Estimator from Censored Samples. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada160302.

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Marron, J. S., and W. J. Padgett. Asymptotically Optimal Bandwidth Selection for Kernel Density Estimators from Randomly Right-Censored Samples. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada168941.

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