Bibliografie tematiche / Density estimation

Indice

  1. Articoli di riviste
  2. Tesi
  3. Libri
  4. Capitoli di libri
  5. Atti di convegni
  6. Rapporti di organizzazioni

Letteratura scientifica selezionata sul tema "Density estimation"

Autore: Grafiati
Pubblicato: 4 giugno 2021
Ultima modifica: 25 luglio 2025

Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili

Scegli il tipo di fonte:

Consulta la lista di attuali articoli, libri, tesi, atti di convegni e altre fonti scientifiche attinenti al tema "Density estimation".

Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.

Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.

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 (2013): 2734–75. http://dx.doi.org/10.1162/neco_a_00492.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
2

Sasaki, Hiroaki, Yung-Kyun Noh, Gang Niu, and Masashi Sugiyama. "Direct Density Derivative Estimation." Neural Computation 28, no. 6 (2016): 1101–40. http://dx.doi.org/10.1162/neco_a_00835.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
3

Yamane, Ikko, Hiroaki Sasaki, and Masashi Sugiyama. "Regularized Multitask Learning for Multidimensional Log-Density Gradient Estimation." Neural Computation 28, no. 7 (2016): 1388–410. http://dx.doi.org/10.1162/neco_a_00844.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
4

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 (2011): 181–93. http://dx.doi.org/10.1017/s1748499511000030.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
5

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 (2011): 549–54. http://dx.doi.org/10.1609/aaai.v25i1.7905.

Testo completo
Abstract (sommario):
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.
Gli stili APA, Harvard, Vancouver, ISO e altri
6

Hovda, Sigve. "Properties of Transmetric Density Estimation." International Journal of Statistics and Probability 5, no. 3 (2016): 63. http://dx.doi.org/10.5539/ijsp.v5n3p63.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
7

Beaumont, Chris, and B. W. Silverman. "Density Estimation." Journal of the Operational Research Society 37, no. 11 (1986): 1102. http://dx.doi.org/10.2307/2582699.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
8

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
9

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.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
10

Tayeb, Djebbouri, and Djerfi Kouider. "On Kernel density estimation on Product Riemannian manifolds." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 3 (2024): e12912. https://doi.org/10.54021/seesv5n3-114.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
Più fonti

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/.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
2

Rademeyer, Estian. "Bayesian kernel density estimation." Diss., University of Pretoria, 2017. http://hdl.handle.net/2263/64692.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
3

Stride, Christopher B. "Semi-parametric density estimation." Thesis, University of Warwick, 1995. http://wrap.warwick.ac.uk/109619/.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
4

Rossiter, Jane E. "Epidemiological applications of density estimation." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.291543.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
5

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
6

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.

Testo completo
Abstract (sommario):
<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
Gli stili APA, Harvard, Vancouver, ISO e altri
7

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
8

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

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
9

Lu, Shan. "Essays on volatility forecasting and density estimation." Thesis, University of Aberdeen, 2019. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=240161.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
10

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.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
Più fonti

Libri sul tema "Density estimation"

1

Stride, Christopher B. Semi-parametric density estimation. typescript, 1995.

Cerca il testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
2

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

Cerca il testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
3

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
4

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

Cerca il testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
5

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

Cerca il testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
6

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

Cerca il testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
7

Silverman, B. W. Density Estimation for Statistics and Data Analysis. Springer US, 1986. http://dx.doi.org/10.1007/978-1-4899-3324-9.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
8

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

Cerca il testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
9

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

Cerca il testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
10

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
Più fonti

Capitoli di libri sul tema "Density estimation"

1

Györfi, Lázió, Wolfgang Härdle, Pascal Sarda, and Philippe Vieu. "Density Estimation." In Nonparametric Curve Estimation from Time Series. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-3686-3_4.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
2

Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, et al. "Density Estimation." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_210.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
3

Kolassa, John E. "Density Estimation." In An Introduction to Nonparametric Statistics. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429202759-8.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
4

Sammut, Claude. "Density Estimation." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_210.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
5

Lee, Myoung-jae. "Nonparametric Density Estimation." In Methods of Moments and Semiparametric Econometrics for Limited Dependent Variable Models. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-2550-6_7.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
6

Gu, Chong. "Probability Density Estimation." In Smoothing Spline ANOVA Models. Springer New York, 2002. http://dx.doi.org/10.1007/978-1-4757-3683-0_6.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
7

Hirukawa, Masayuki. "Univariate Density Estimation." In Asymmetric Kernel Smoothing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5466-2_2.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
8

Härdle, Wolfgang. "Kernel Density Estimation." In Springer Series in Statistics. Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4612-4432-5_2.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
9

Simonoff, Jeffrey S. "Multivariate Density Estimation." In Springer Series in Statistics. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4026-6_4.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
10

Härdle, Wolfgang, Axel Werwatz, Marlene Müller, and Stefan Sperlich. "Nonparametric Density Estimation." In Springer Series in Statistics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17146-8_3.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri

Atti di convegni sul tema "Density estimation"

1

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). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620151.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
2

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
3

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). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10930190.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
4

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}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/525.

Testo completo
Abstract (sommario):
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
Gli stili APA, Harvard, Vancouver, ISO e altri
5

Ram, Parikshit, and Alexander G. Gray. "Density estimation trees." In the 17th ACM SIGKDD international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020507.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
6

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.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
7

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. Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.95.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
8

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.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
9

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.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
10

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.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri

Rapporti di organizzazioni sul tema "Density estimation"

1

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
2

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
3

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
4

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
5

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
6

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
7

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
8

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
9

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

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
10

Mizera, Ivan, and Roger Koenker. Shape constrained density estimation via penalized Rényi divergence. The IFS, 2018. http://dx.doi.org/10.1920/wp.cem.2018.5418.

Testo completo
Gli stili APA, Harvard, Vancouver, ISO e altri
Ti possono interessare anche gli elenchi estesi di opere sul tema "Density estimation":
Articoli di riviste Tesi Libri
Offriamo sconti su tutti i piani premium per gli autori le cui opere sono incluse in raccolte letterarie tematiche. Contattaci per ottenere un codice promozionale unico!

Vai alla bibliografia