Literatura científica selecionada sobre o tema "Estimation of Density"
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Artigos de revistas sobre o assunto "Estimation of Density"
Sugiyama, Masashi, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu e Ichiro Takeuchi. "Density-Difference Estimation". Neural Computation 25, n.º 10 (outubro de 2013): 2734–75. http://dx.doi.org/10.1162/neco_a_00492.
Texto completo da fonteSasaki, Hiroaki, Yung-Kyun Noh, Gang Niu e Masashi Sugiyama. "Direct Density Derivative Estimation". Neural Computation 28, n.º 6 (junho de 2016): 1101–40. http://dx.doi.org/10.1162/neco_a_00835.
Texto completo da fonteYamane, Ikko, Hiroaki Sasaki e Masashi Sugiyama. "Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation". Neural Computation 28, n.º 7 (julho de 2016): 1388–410. http://dx.doi.org/10.1162/neco_a_00844.
Texto completo da fonteHovda, Sigve. "Properties of Transmetric Density Estimation". International Journal of Statistics and Probability 5, n.º 3 (13 de abril de 2016): 63. http://dx.doi.org/10.5539/ijsp.v5n3p63.
Texto completo da fonteLiu, Qing, David Pitt, Xibin Zhang e Xueyuan Wu. "A Bayesian Approach to Parameter Estimation for Kernel Density Estimation via Transformations". Annals of Actuarial Science 5, n.º 2 (18 de abril de 2011): 181–93. http://dx.doi.org/10.1017/s1748499511000030.
Texto completo da fonteBeaumont, Chris, e B. W. Silverman. "Density Estimation." Journal of the Operational Research Society 37, n.º 11 (novembro de 1986): 1102. http://dx.doi.org/10.2307/2582699.
Texto completo da fonteSheather, Simon J. "Density Estimation". Statistical Science 19, n.º 4 (novembro de 2004): 588–97. http://dx.doi.org/10.1214/088342304000000297.
Texto completo da fonteYamada, Makoto, e Masashi Sugiyama. "Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis". Proceedings of the AAAI Conference on Artificial Intelligence 25, n.º 1 (4 de agosto de 2011): 549–54. http://dx.doi.org/10.1609/aaai.v25i1.7905.
Texto completo da fonteLi, Rui, e Youming Liu. "Wavelet Optimal Estimations for Density Functions under Severely Ill-Posed Noises". Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/260573.
Texto completo da fonteHovda, Sigve. "Transmetric Density Estimation". International Journal of Statistics and Probability 5, n.º 2 (10 de fevereiro de 2016): 35. http://dx.doi.org/10.5539/ijsp.v5n2p35.
Texto completo da fonteTeses / dissertações sobre o assunto "Estimation of Density"
Wang, Xiaoxia. "Manifold aligned density estimation". Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/847/.
Texto completo da fonteRademeyer, Estian. "Bayesian kernel density estimation". Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64692.
Texto completo da fonteDissertation (MSc)--University of Pretoria, 2017.
The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the authors and are not necessarily to be attributed to the NRF.
Statistics
MSc
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Stride, Christopher B. "Semi-parametric density estimation". Thesis, University of Warwick, 1995. http://wrap.warwick.ac.uk/109619/.
Texto completo da fonteRossiter, Jane E. "Epidemiological applications of density estimation". Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291543.
Texto completo da fonteSung, Iyue. "Importance sampling kernel density estimation /". The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486398528559777.
Texto completo da fonteKile, Håkon. "Bandwidth Selection in Kernel Density Estimation". Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10015.
Texto completo da fonteIn kernel density estimation, the most crucial step is to select a proper bandwidth (smoothing parameter). There are two conceptually different approaches to this problem: a subjective and an objective approach. In this report, we only consider the objective approach, which is based upon minimizing an error, defined by an error criterion. The most common objective bandwidth selection method is to minimize some squared error expression, but this method is not without its critics. This approach is said to not perform satisfactory in the tail(s) of the density, and to put too much weight on observations close to the mode(s) of the density. An approach which minimizes an absolute error expression, is thought to be without these drawbacks. We will provide a new explicit formula for the mean integrated absolute error. The optimal mean integrated absolute error bandwidth will be compared to the optimal mean integrated squared error bandwidth. We will argue that these two bandwidths are essentially equal. In addition, we study data-driven bandwidth selection, and we will propose a new data-driven bandwidth selector. Our new bandwidth selector has promising behavior with respect to the visual error criterion, especially in the cases of limited sample sizes.
Achilleos, Achilleas. "Deconvolution kernal density and regression estimation". Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544421.
Texto completo da fonteBuchman, Susan. "High-Dimensional Adaptive Basis Density Estimation". Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/169.
Texto completo da fonteLu, Shan. "Essays on volatility forecasting and density estimation". Thesis, University of Aberdeen, 2019. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=240161.
Texto completo da fonteChan, Kwokleung. "Bayesian learning in classification and density estimation /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3061619.
Texto completo da fonteLivros sobre o assunto "Estimation of Density"
Stride, Christopher B. Semi-parametric density estimation. [s.l.]: typescript, 1995.
Encontre o texto completo da fonteA. J. H. van Es. Aspects of nonparametric density estimation. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1991.
Encontre o texto completo da fonteDevroye, Luc, e Gábor Lugosi. Combinatorial Methods in Density Estimation. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0125-7.
Texto completo da fonteDevroye, Luc. A course in density estimation. Boston: Birkhäuser, 1987.
Encontre o texto completo da fonteDevroye, Luc. Nonparametric density estimation: The L₁ view. New York: Wiley, 1985.
Encontre o texto completo da fonteDevroye, Luc. Nonparametric density estimation: The L1 view. New York: Wiley, 1985.
Encontre o texto completo da fonteSugiyama, Masashi. Density ratio estimation in machine learning. Cambridge: Cambridge University Press, 2012.
Encontre o texto completo da fonteSilverman, B. W. Density Estimation for Statistics and Data Analysis. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-3324-9.
Texto completo da fonteZinde-Walsh, Victoria. Kernel estimation when density does not exist. Montréal: Centre interuniversitaire de recherche en économie quantitative, 2005.
Encontre o texto completo da fonteDensity estimation for statistics and data analysis. Boca Raton: Chapman & Hall/CRC, 1998.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Estimation of Density"
Györfi, Lázió, Wolfgang Härdle, Pascal Sarda e Philippe Vieu. "Density Estimation". In Nonparametric Curve Estimation from Time Series, 53–79. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3686-3_4.
Texto completo da fonteWebb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander et al. "Density Estimation". In Encyclopedia of Machine Learning, 270. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_210.
Texto completo da fonteKolassa, John E. "Density Estimation". In An Introduction to Nonparametric Statistics, 143–48. First edition. | Boca Raton : CRC Press, 2020. |: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429202759-8.
Texto completo da fonteSammut, Claude. "Density Estimation". In Encyclopedia of Machine Learning and Data Mining, 348–49. Boston, MA: Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_210.
Texto completo da fonteLee, Myoung-jae. "Nonparametric Density Estimation". In Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models, 123–42. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2550-6_7.
Texto completo da fonteGu, Chong. "Probability Density Estimation". In Smoothing Spline ANOVA Models, 177–210. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3683-0_6.
Texto completo da fonteHirukawa, Masayuki. "Univariate Density Estimation". In Asymmetric Kernel Smoothing, 17–39. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5466-2_2.
Texto completo da fonteHärdle, Wolfgang. "Kernel Density Estimation". In Springer Series in Statistics, 43–84. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-4432-5_2.
Texto completo da fonteSimonoff, Jeffrey S. "Multivariate Density Estimation". In Springer Series in Statistics, 96–133. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4026-6_4.
Texto completo da fonteHärdle, Wolfgang, Axel Werwatz, Marlene Müller e Stefan Sperlich. "Nonparametric Density Estimation". In Springer Series in Statistics, 39–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17146-8_3.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Estimation of Density"
Ram, Parikshit, e Alexander G. Gray. "Density estimation trees". In the 17th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020507.
Texto completo da fonteJooSeuk Kim e Clayton Scott. "Robust kernel density estimation". In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518376.
Texto completo da fonteMiao, Yun-Qian, Ahmed K. Farahat e Mohamed S. Kamel. "Discriminative Density-ratio Estimation". In Proceedings of the 2014 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.95.
Texto completo da fonteSun, Ke, e Stéphane Marchand-Maillet. "Information geometric density estimation". In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING (MAXENT 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4905982.
Texto completo da fonteTing, Kai Ming, Takashi Washio, Jonathan R. Wells e Hang Zhang. "Isolation Kernel Density Estimation". In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 2021. http://dx.doi.org/10.1109/icdm51629.2021.00073.
Texto completo da fonteYilan, Mikail, e Mehmet Kemal Ozdemir. "A simple approach to traffic density estimation by using Kernel Density Estimation". In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130220.
Texto completo da fonteTakahashi, Hiroshi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada e Satoshi Yagi. "Student-t Variational Autoencoder for Robust Density Estimation". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/374.
Texto completo da fonteSuga, Norisato, Kazuto Yano, Julian Webber, Yafei Hou, Toshihide Higashimori e Yoshinori Suzuki. "Estimation of Probability Density Function Using Multi-bandwidth Kernel Density Estimation for Throughput". In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2020. http://dx.doi.org/10.1109/icaiic48513.2020.9065033.
Texto completo da fonteKrauthausen, Peter, e Uwe D. Hanebeck. "Regularized non-parametric multivariate density and conditional density estimation". In 2010 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2010). IEEE, 2010. http://dx.doi.org/10.1109/mfi.2010.5604457.
Texto completo da fonteCharikar, Moses, Michael Kapralov, Navid Nouri e Paris Siminelakis. "Kernel Density Estimation through Density Constrained Near Neighbor Search". In 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2020. http://dx.doi.org/10.1109/focs46700.2020.00025.
Texto completo da fonteRelatórios de organizações sobre o assunto "Estimation of Density"
Marchette, David J., Carey E. Priebe, George W. Rogers e Jeffrey L. Solka. Filtered Kernel Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, outubro de 1994. http://dx.doi.org/10.21236/ada288293.
Texto completo da fonteMarchette, David J., Carey E. Priebe, George W. Rogers e Jefferey L. Solka. Filtered Kernel Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, outubro de 1994. http://dx.doi.org/10.21236/ada290438.
Texto completo da fonteCollins, David H. Density estimation with trigonometric kernels. Office of Scientific and Technical Information (OSTI), fevereiro de 2016. http://dx.doi.org/10.2172/1237269.
Texto completo da fonteYu, Bin. Optimal Universal Coding and Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, novembro de 1994. http://dx.doi.org/10.21236/ada290694.
Texto completo da fonteRakhlin, Alexander, Dmitry Panchenko e Sayan Mukherjee. Risk Bounds for Mixture Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, janeiro de 2004. http://dx.doi.org/10.21236/ada459846.
Texto completo da fonteSmith, Richard J., e Vitaliy Oryshchenko. Improved density and distribution function estimation. The IFS, julho de 2018. http://dx.doi.org/10.1920/wp.cem.2018.4718.
Texto completo da fontePowell, James L., Fengshi Niu e Bryan S. Graham. Kernel density estimation for undirected dyadic data. The IFS, agosto de 2019. http://dx.doi.org/10.1920/wp.cem.2019.3919.
Texto completo da fonteChen, X. R., P. R. Krishnaiah e W. Q. Liang. Estimation of Multivariate Binary Density Using Orthonormal Functions. Fort Belvoir, VA: Defense Technical Information Center, dezembro de 1986. http://dx.doi.org/10.21236/ada186386.
Texto completo da fonteMellinger, David K. Detection, Classification, and Density Estimation of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, outubro de 2012. http://dx.doi.org/10.21236/ada579344.
Texto completo da fonteMizera, Ivan, e Roger Koenker. Shape constrained density estimation via penalized Rényi divergence. The IFS, setembro de 2018. http://dx.doi.org/10.1920/wp.cem.2018.5418.
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