Auswahl der wissenschaftlichen Literatur zum Thema „Estimation of Density“
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Zeitschriftenartikel zum Thema "Estimation of Density"
Sugiyama, Masashi, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu und Ichiro Takeuchi. „Density-Difference Estimation“. Neural Computation 25, Nr. 10 (Oktober 2013): 2734–75. http://dx.doi.org/10.1162/neco_a_00492.
Der volle Inhalt der QuelleSasaki, Hiroaki, Yung-Kyun Noh, Gang Niu und Masashi Sugiyama. „Direct Density Derivative Estimation“. Neural Computation 28, Nr. 6 (Juni 2016): 1101–40. http://dx.doi.org/10.1162/neco_a_00835.
Der volle Inhalt der QuelleYamane, Ikko, Hiroaki Sasaki und Masashi Sugiyama. „Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation“. Neural Computation 28, Nr. 7 (Juli 2016): 1388–410. http://dx.doi.org/10.1162/neco_a_00844.
Der volle Inhalt der QuelleHovda, Sigve. „Properties of Transmetric Density Estimation“. International Journal of Statistics and Probability 5, Nr. 3 (13.04.2016): 63. http://dx.doi.org/10.5539/ijsp.v5n3p63.
Der volle Inhalt der QuelleLiu, Qing, David Pitt, Xibin Zhang und Xueyuan Wu. „A Bayesian Approach to Parameter Estimation for Kernel Density Estimation via Transformations“. Annals of Actuarial Science 5, Nr. 2 (18.04.2011): 181–93. http://dx.doi.org/10.1017/s1748499511000030.
Der volle Inhalt der QuelleBeaumont, Chris, und B. W. Silverman. „Density Estimation.“ Journal of the Operational Research Society 37, Nr. 11 (November 1986): 1102. http://dx.doi.org/10.2307/2582699.
Der volle Inhalt der QuelleSheather, Simon J. „Density Estimation“. Statistical Science 19, Nr. 4 (November 2004): 588–97. http://dx.doi.org/10.1214/088342304000000297.
Der volle Inhalt der QuelleYamada, Makoto, und Masashi Sugiyama. „Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis“. Proceedings of the AAAI Conference on Artificial Intelligence 25, Nr. 1 (04.08.2011): 549–54. http://dx.doi.org/10.1609/aaai.v25i1.7905.
Der volle Inhalt der QuelleLi, Rui, und 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.
Der volle Inhalt der QuelleHovda, Sigve. „Transmetric Density Estimation“. International Journal of Statistics and Probability 5, Nr. 2 (10.02.2016): 35. http://dx.doi.org/10.5539/ijsp.v5n2p35.
Der volle Inhalt der QuelleDissertationen zum Thema "Estimation of Density"
Wang, Xiaoxia. „Manifold aligned density estimation“. Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/847/.
Der volle Inhalt der QuelleRademeyer, Estian. „Bayesian kernel density estimation“. Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64692.
Der volle Inhalt der QuelleDissertation (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
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Stride, Christopher B. „Semi-parametric density estimation“. Thesis, University of Warwick, 1995. http://wrap.warwick.ac.uk/109619/.
Der volle Inhalt der QuelleRossiter, Jane E. „Epidemiological applications of density estimation“. Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291543.
Der volle Inhalt der QuelleSung, Iyue. „Importance sampling kernel density estimation /“. The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486398528559777.
Der volle Inhalt der QuelleKile, 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.
Der volle Inhalt der QuelleIn 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.
Der volle Inhalt der QuelleBuchman, Susan. „High-Dimensional Adaptive Basis Density Estimation“. Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/169.
Der volle Inhalt der QuelleLu, Shan. „Essays on volatility forecasting and density estimation“. Thesis, University of Aberdeen, 2019. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=240161.
Der volle Inhalt der QuelleChan, 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.
Der volle Inhalt der QuelleBücher zum Thema "Estimation of Density"
Stride, Christopher B. Semi-parametric density estimation. [s.l.]: typescript, 1995.
Den vollen Inhalt der Quelle findenA. J. H. van Es. Aspects of nonparametric density estimation. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1991.
Den vollen Inhalt der Quelle findenDevroye, Luc, und 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.
Der volle Inhalt der QuelleA course in density estimation. Boston: Birkhäuser, 1987.
Den vollen Inhalt der Quelle findenDevroye, Luc. Nonparametric density estimation: The L₁ view. New York: Wiley, 1985.
Den vollen Inhalt der Quelle findenDevroye, Luc. Nonparametric density estimation: The L1 view. New York: Wiley, 1985.
Den vollen Inhalt der Quelle finden1981-, Suzuki Taiji, und Kanamori Takafumi 1971-, Hrsg. Density ratio estimation in machine learning. Cambridge: Cambridge University Press, 2012.
Den vollen Inhalt der Quelle findenSilverman, 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.
Der volle Inhalt der QuelleZinde-Walsh, Victoria. Kernel estimation when density does not exist. Montréal: Centre interuniversitaire de recherche en économie quantitative, 2005.
Den vollen Inhalt der Quelle findenDensity estimation for statistics and data analysis. Boca Raton: Chapman & Hall/CRC, 1998.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Estimation of Density"
Györfi, Lázió, Wolfgang Härdle, Pascal Sarda und 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.
Der volle Inhalt der QuelleWebb, 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.
Der volle Inhalt der QuelleKolassa, 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.
Der volle Inhalt der QuelleSammut, 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.
Der volle Inhalt der QuelleLee, 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.
Der volle Inhalt der QuelleGu, 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.
Der volle Inhalt der QuelleHirukawa, 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.
Der volle Inhalt der QuelleHärdle, 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.
Der volle Inhalt der QuelleSimonoff, 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.
Der volle Inhalt der QuelleHärdle, Wolfgang, Axel Werwatz, Marlene Müller und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Estimation of Density"
Ram, Parikshit, und 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.
Der volle Inhalt der QuelleJooSeuk Kim und 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.
Der volle Inhalt der QuelleMiao, Yun-Qian, Ahmed K. Farahat und 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.
Der volle Inhalt der QuelleSun, Ke, und 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.
Der volle Inhalt der QuelleTing, Kai Ming, Takashi Washio, Jonathan R. Wells und 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.
Der volle Inhalt der QuelleYilan, Mikail, und 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.
Der volle Inhalt der QuelleTakahashi, Hiroshi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada und 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.
Der volle Inhalt der QuelleSuga, Norisato, Kazuto Yano, Julian Webber, Yafei Hou, Toshihide Higashimori und 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.
Der volle Inhalt der QuelleKrauthausen, Peter, und 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.
Der volle Inhalt der QuelleCharikar, Moses, Michael Kapralov, Navid Nouri und 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.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Estimation of Density"
Marchette, David J., Carey E. Priebe, George W. Rogers und Jeffrey L. Solka. Filtered Kernel Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, Oktober 1994. http://dx.doi.org/10.21236/ada288293.
Der volle Inhalt der QuelleMarchette, David J., Carey E. Priebe, George W. Rogers und Jefferey L. Solka. Filtered Kernel Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, Oktober 1994. http://dx.doi.org/10.21236/ada290438.
Der volle Inhalt der QuelleCollins, David H. Density estimation with trigonometric kernels. Office of Scientific and Technical Information (OSTI), Februar 2016. http://dx.doi.org/10.2172/1237269.
Der volle Inhalt der QuelleYu, Bin. Optimal Universal Coding and Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada290694.
Der volle Inhalt der QuelleRakhlin, Alexander, Dmitry Panchenko und Sayan Mukherjee. Risk Bounds for Mixture Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, Januar 2004. http://dx.doi.org/10.21236/ada459846.
Der volle Inhalt der QuelleSmith, Richard J., und Vitaliy Oryshchenko. Improved density and distribution function estimation. The IFS, Juli 2018. http://dx.doi.org/10.1920/wp.cem.2018.4718.
Der volle Inhalt der QuellePowell, James L., Fengshi Niu und Bryan S. Graham. Kernel density estimation for undirected dyadic data. The IFS, August 2019. http://dx.doi.org/10.1920/wp.cem.2019.3919.
Der volle Inhalt der QuelleChen, X. R., P. R. Krishnaiah und W. Q. Liang. Estimation of Multivariate Binary Density Using Orthonormal Functions. Fort Belvoir, VA: Defense Technical Information Center, Dezember 1986. http://dx.doi.org/10.21236/ada186386.
Der volle Inhalt der QuelleMellinger, David K. Detection, Classification, and Density Estimation of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, Oktober 2012. http://dx.doi.org/10.21236/ada579344.
Der volle Inhalt der QuelleMizera, Ivan, und Roger Koenker. Shape constrained density estimation via penalized Rényi divergence. The IFS, September 2018. http://dx.doi.org/10.1920/wp.cem.2018.5418.
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