Bibliografie tematiche / Density estimation

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Letteratura scientifica selezionata sul tema "Density estimation"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Density estimation"

1

Sugiyama, Masashi, Takafumi Kanamori, Taiji Suzuki, Marthinus Christoffel du Plessis, Song Liu, and Ichiro Takeuchi. "Density-Difference Estimation." Neural Computation 25, no. 10 (October 2013): 2734–75. http://dx.doi.org/10.1162/neco_a_00492.

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We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, this procedure does not necessarily work well because the first step is performed without regard to the second step, and thus a small estimation error incurred in the first stage can cause a big error in the second stage. In this letter, we propose a single-shot procedure for directly estimating the density difference without separately estimating two densities. We derive a nonp
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Sasaki, Hiroaki, Yung-Kyun Noh, Gang Niu, and Masashi Sugiyama. "Direct Density Derivative Estimation." Neural Computation 28, no. 6 (June 2016): 1101–40. http://dx.doi.org/10.1162/neco_a_00835.

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Estimating the derivatives of probability density functions is an essential step in statistical data analysis. A naive approach to estimate the derivatives is to first perform density estimation and then compute its derivatives. However, this approach can be unreliable because a good density estimator does not necessarily mean a good density derivative estimator. To cope with this problem, in this letter, we propose a novel method that directly estimates density derivatives without going through density estimation. The proposed method provides computationally efficient estimation for the deriv
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Yamane, Ikko, Hiroaki Sasaki, and Masashi Sugiyama. "Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation." Neural Computation 28, no. 7 (July 2016): 1388–410. http://dx.doi.org/10.1162/neco_a_00844.

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Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring nongaussianity. A naive two-step approach of first estimating the density and then taking its log gradient is unreliable because an accurate density estimate does not necessarily lead to an accurate log-density gradient estimate. To cope with this problem, a method to directly estimate the log-density gradient without density estimation has been explored and demonstrated to work much better than the two-step method. The objective of this letter is t
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Liu, Qing, David Pitt, Xibin Zhang, and Xueyuan Wu. "A Bayesian Approach to Parameter Estimation for Kernel Density Estimation via Transformations." Annals of Actuarial Science 5, no. 2 (April 18, 2011): 181–93. http://dx.doi.org/10.1017/s1748499511000030.

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AbstractIn this paper, we present a Markov chain Monte Carlo (MCMC) simulation algorithm for estimating parameters in the kernel density estimation of bivariate insurance claim data via transformations. Our data set consists of two types of auto insurance claim costs and exhibits a high-level of skewness in the marginal empirical distributions. Therefore, the kernel density estimator based on original data does not perform well. However, the density of the original data can be estimated through estimating the density of the transformed data using kernels. It is well known that the performance
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Yamada, Makoto, and Masashi Sugiyama. "Direct Density-Ratio Estimation with Dimensionality Reduction via Hetero-Distributional Subspace Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 549–54. http://dx.doi.org/10.1609/aaai.v25i1.7905.

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Methods for estimating the ratio of two probability density functions have been actively explored recently since they can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, feature selection, and conditional probability estimation. In this paper, we propose a new density-ratio estimator which incorporates dimensionality reduction into the density-ratio estimation procedure. Through experiments, the proposed method is shown to compare favorably with existing density-ratio estimators in terms of both accuracy and computational costs.
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Hovda, Sigve. "Properties of Transmetric Density Estimation." International Journal of Statistics and Probability 5, no. 3 (April 13, 2016): 63. http://dx.doi.org/10.5539/ijsp.v5n3p63.

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Transmetric density estimation is a generalization of kernel density estimation that is proposed in Hovda(2014) and Hovda (2016), This framework involves the possibility of making assumptions on the kernel of the distribution to improve convergence orders and to reduce the number of dimensions in the graphical display. In this paper we show that several state-of-the-art nonparametric, semiparametric and even parametric methods are special cases of this formulation, meaning that there is a unified approach. Moreover, it is shown that parameters can be trained using unbiased cross-validation. Wh
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Beaumont, Chris, and B. W. Silverman. "Density Estimation." Journal of the Operational Research Society 37, no. 11 (November 1986): 1102. http://dx.doi.org/10.2307/2582699.

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Sheather, Simon J. "Density Estimation." Statistical Science 19, no. 4 (November 2004): 588–97. http://dx.doi.org/10.1214/088342304000000297.

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Li, Rui, and 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.

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Motivated by Lounici and Nickl's work (2011), this paper considers the problem of estimation of a densityfbased on an independent and identically distributed sampleY1,…,Ynfromg=f*φ. We show a wavelet optimal estimation for a density (function) over Besov ballBr,qs(L)andLprisk (1≤p<∞) in the presence of severely ill-posed noises. A wavelet linear estimation is firstly presented. Then, we prove a lower bound, which shows our wavelet estimator optimal. In other words, nonlinear wavelet estimations are not needed in that case. It turns out that our results extend some theorems of Pensky and Vid
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Tayeb, Djebbouri, and Djerfi Kouider. "On Kernel density estimation on Product Riemannian manifolds." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 3 (December 31, 2024): e12912. https://doi.org/10.54021/seesv5n3-114.

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Estimating the kernel density function of a random vector taking values on Riemannian manifolds are considered. More Precisely, we consider the problem of the estimation of the probability density of n i.i.d. random objects on the product of two compact Riemannian manifolds without boundary. The proposed methodology adapts the Pelletier’s approach which is the kernel density estimation on non Euclidean setting. Under sufficient regularity assumptions on the underlying density, L2 convergence rates are obtained. Riemannian products are the most natural and fruitful generalization of Cartesian p
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Tesi sul tema "Density estimation"

1

Wang, Xiaoxia. "Manifold aligned density estimation." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/847/.

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With the advent of the information technology, the amount of data we are facing today is growing in both the scale and the dimensionality dramatically. It thus raises new challenges for some traditional machine learning tasks. This thesis is mainly concerned with manifold aligned density estimation problems. In particular, the work presented in this thesis includes efficiently learning the density distribution on very large-scale datasets and estimating the manifold aligned density through explicit manifold modeling. First, we propose an efficient and sparse density estimator: Fast Parzen Wind
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Rademeyer, Estian. "Bayesian kernel density estimation." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64692.

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This dissertation investigates the performance of two-class classi cation credit scoring data sets with low default ratios. The standard two-class parametric Gaussian and naive Bayes (NB), as well as the non-parametric Parzen classi ers are extended, using Bayes' rule, to include either a class imbalance or a Bernoulli prior. This is done with the aim of addressing the low default probability problem. Furthermore, the performance of Parzen classi cation with Silverman and Minimum Leave-one-out Entropy (MLE) Gaussian kernel bandwidth estimation is also investigated. It is shown that the n
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Stride, Christopher B. "Semi-parametric density estimation." Thesis, University of Warwick, 1995. http://wrap.warwick.ac.uk/109619/.

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The local likelihood method of Copas (1995a) allows for the incorporation into our parametric model of influence from data local to the point t at which we are estimating the true density function g(t). This is achieved through an analogy with censored data; we define the probability of a data point being considered observed, given that it has taken value xi, as where K is a scaled kernel function with smoothing parameter h. This leads to a likelihood function which gives more weight to observations close to t, hence the term ‘local likelihood’. After constructing this local likelihood functio
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Rossiter, Jane E. "Epidemiological applications of density estimation." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291543.

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Sung, Iyue. "Importance sampling kernel density estimation /." The Ohio State University, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=osu1486398528559777.

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Kile, 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.

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<p>In 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 o
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Achilleos, Achilleas. "Deconvolution kernal density and regression estimation." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.544421.

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Buchman, Susan. "High-Dimensional Adaptive Basis Density Estimation." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/169.

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In the realm of high-dimensional statistics, regression and classification have received much attention, while density estimation has lagged behind. Yet there are compelling scientific questions which can only be addressed via density estimation using high-dimensional data, such as the paths of North Atlantic tropical cyclones. If we cast each track as a single high-dimensional data point, density estimation allows us to answer such questions via integration or Monte Carlo methods. In this dissertation, I present three new methods for estimating densities and intensities for high-dimensional d
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Lu, Shan. "Essays on volatility forecasting and density estimation." Thesis, University of Aberdeen, 2019. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=240161.

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This thesis studies two subareas within the forecasting literature: volatility forecasting and risk-neutral density estimation and asks the question of how accurate volatility forecasts and risk-neutral density estimates can be made based on the given information. Two sources of information are employed to make those forecasts: historical information contained in time series of asset prices, and forward-looking information embedded in prices of traded options. Chapter 2 tests the comparative performance of two volatility scaling laws - the square-root-of-time (√T) and an empirical law, TH, cha
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Chan, 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.

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Libri sul tema "Density estimation"

1

Stride, Christopher B. Semi-parametric density estimation. [s.l.]: typescript, 1995.

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A. J. H. van Es. Aspects of nonparametric density estimation. Amsterdam, The Netherlands: Centrum voor Wiskunde en Informatica, 1991.

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Devroye, Luc, and 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.

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Devroye, Luc. Nonparametric density estimation: The L1 view. New York: Wiley, 1985.

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László, Györfi, ed. Nonparametric density estimation: The L₁ view. New York: Wiley, 1985.

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1981-, Suzuki Taiji, and Kanamori Takafumi 1971-, eds. Density ratio estimation in machine learning. Cambridge: Cambridge University Press, 2012.

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Silverman, 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.

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Zinde-Walsh, Victoria. Kernel estimation when density does not exist. Montréal: Centre interuniversitaire de recherche en économie quantitative, 2005.

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W, Scott David. Multivariate density estimation: Theory, practice, and visualization. New York: Wiley, 1992.

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Gramacki, Artur. Nonparametric Kernel Density Estimation and Its Computational Aspects. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71688-6.

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Capitoli di libri sul tema "Density estimation"

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Györfi, Lázió, Wolfgang Härdle, Pascal Sarda, and 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.

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Webb, 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.

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Kolassa, 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.

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Sammut, 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.

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Lee, 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.

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Gu, 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.

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Hirukawa, 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.

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Härdle, 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.

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Simonoff, 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.

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Härdle, Wolfgang, Axel Werwatz, Marlene Müller, and 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.

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Atti di convegni sul tema "Density estimation"

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Elharrouss, Omar, Hanadi Hassen Mohammed, Somaya Al-Maadeed, Khalid Abualsaud, Amr Mohamed, and Tamer Khattab. "Crowd density estimation with a block-based density map generation." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV), 1–7. IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620151.

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Wang, Meilin, Wei Huang, and Zheng Zhang. "Projection Pursuit Based Density Ratio Estimation." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW), 814–19. IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00112.

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Choi, Jungyu, Joonhwi Kim, and Sungbin Im. "Robust TDOA Estimation Using Kernel Density Estimation for Noisy Environments." In 2025 IEEE International Conference on Consumer Electronics (ICCE), 1–5. IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10930190.

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Mészáros, Anna, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov, and Jens Kober. "ROME: Robust Multi-Modal Density Estimator." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/525.

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The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demonstrated to lack robustness, while more complex methods have not been evaluated in multi-modal estimation problems. In this paper, we present ROME (RObust Multi-modal Estimator), a non-parametric approach for density estimation which addresses the challenge of estimating multi-modal, non-normal, and highly correlated distributions. ROME utilizes clustering to segment a multi-modal set of samples into multiple uni-moda
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Ram, Parikshit, and 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.

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JooSeuk Kim and 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.

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Miao, Yun-Qian, Ahmed K. Farahat, and 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.

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Sun, Ke, and 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.

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Ting, Kai Ming, Takashi Washio, Jonathan R. Wells, and 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.

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Yilan, Mikail, and 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.

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Rapporti di organizzazioni sul tema "Density estimation"

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Marchette, David J., Carey E. Priebe, George W. Rogers, and Jeffrey L. Solka. Filtered Kernel Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada288293.

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Marchette, David J., Carey E. Priebe, George W. Rogers, and Jefferey L. Solka. Filtered Kernel Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada290438.

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Collins, David H. Density estimation with trigonometric kernels. Office of Scientific and Technical Information (OSTI), February 2016. http://dx.doi.org/10.2172/1237269.

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Yu, Bin. Optimal Universal Coding and Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada290694.

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Rakhlin, Alexander, Dmitry Panchenko, and Sayan Mukherjee. Risk Bounds for Mixture Density Estimation. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada459846.

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Smith, Richard J., and Vitaliy Oryshchenko. Improved density and distribution function estimation. The IFS, July 2018. http://dx.doi.org/10.1920/wp.cem.2018.4718.

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Powell, James L., Fengshi Niu, and Bryan S. Graham. Kernel density estimation for undirected dyadic data. The IFS, August 2019. http://dx.doi.org/10.1920/wp.cem.2019.3919.

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Chen, X. R., P. R. Krishnaiah, and W. Q. Liang. Estimation of Multivariate Binary Density Using Orthonormal Functions. Fort Belvoir, VA: Defense Technical Information Center, December 1986. http://dx.doi.org/10.21236/ada186386.

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Mellinger, David K. Detection, Classification, and Density Estimation of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, October 2012. http://dx.doi.org/10.21236/ada579344.

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Mizera, Ivan, and 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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