Density Estimation
Forest Density Estimation
... through density estimation is problematic if the graph is allowed to have cycles, as this can require marginal densities to be estimated with many interacting ...tree-structured density, becomes the ...
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Do-Validation for Kernel Density Estimation
... Bandwidth selection in kernel density estimation is one of the fundamental model selection problems of mathematical statistics. The study of this problem took major steps forward with the papers of Hall and ...
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Lower Bounds and Aggregation in Density Estimation
... Remark 1 The construction of the family of densities f δ : δ ∈ N D/8 is in the same spirit as the lower bound of Tsybakov (2003), Rigollet and Tsybakov (2004). But, as compared to Rigollet and Tsybakov (2004), we ...
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Kernel Density Estimation for Dynamical Systems
... eter estimation, invariant measure estimation, forecasting, noise detection, among ...the density estimation problem for dynamical systems via a classical nonparametric approach, ...
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One Sided Crossvalidation for Density Estimation
... linear density estimator of Jones (1993). In density estimation the local linear density estimator is identical to the standard kernel density estimator away from boundaries, see ...
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AMID Based Crowd Density Estimation
... Foreground based methods: In [2-3], first the foreground is extracted by removing background using a reference image, then considering function of the number of foreground pixels crowd density is computed. In [4], ...
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Empirical Bayes Conditional Density Estimation
... conditional density esti- mation that attain minimax rates (up to log-factors) of posterior concentration adopting to both the dimension of the set of relevant covariates and to the regu- larity level of the ...
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Density Estimation methods based on Mass
... Density estimation is ubiquitously applied to various taskssuch as clustering, classification, anomaly detection andinformation ...efficient density estimation methods thus ...kernel ...
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Density Estimation methods based on Mass
... Density estimation is ubiquitously applied to various taskssuch as clustering, classification, anomaly detection andinformation ...efficient density estimation methods thus ...kernel ...
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Categorization as nonparametric Bayesian density estimation
... nonparametric density estimation has motivated the claim that exemplar models are to be preferred as rational models of category learning (Nosofsky, ...
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Feature Descriptor for Crowd Density Estimation
... In this paper, we use the modified and extended version of LBP, called completed local binary pattern (CBLP) [10][51] for crowd density estimation. For estimating crowd density estimation, we ...
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Methods for Shape-Constrained Kernel Density Estimation
... shape-constrained density estimates by starting with a pilot estimate obtained by standard methods, and subsequently adjusting its shape until the constraints are ...constrained estimation problem is ...
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Crowd Density Estimation using Image Processing
... crowd density estimation can help organizers of public events, railway security, college campuses, ...crowd density, by using application based on image ...
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Unimodal density estimation using Bernstein polynomials
... multimodal density estimate into unimodal ...unimodal density as a transformation of some known, but subjective, unimodal ...for density estimation under the monotonicity ...true ...
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Improving Density Estimation by Incorporating Spatial Information
... for density estimation methods that do not incorporate the spatial information to distinguish between invalid regions and areas that have not had any crimes but are still likely to have ...probability ...
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Convergence rates of wavelet density estimation for negatively dependent sample
... The definition was introduced by Bozorgnia [3]. Further discussion and related concepts can be found in [2, 10]. ND random variables are very useful in reliability theory and ap- plications. Because of the wide ...
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Spectral Density Estimation of Continuous Time Series
... This paper studies spectral density estimation of a strictly stationary r-vector valued continuous time series including missing observations. The finite Fourier transform is constructed in L-joint segments ...
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Automatic bandwidth selection for circular density estimation
... nonparametric density estimation is to make good choices of the smoothing parameter h ; see Jones et ...unknown density f ...gaussian density, this leads to a plug-in selector h = ...for ...
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Problems in density estimation for independent and dependent data
... investigate density estimation from independent data, using high order kernel ...negative density estimates where data are ...the density and we provide four ways of correcting this negativity ...
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Convergence Rates of Density Estimation in Besov Spaces
... B R for the risk was established by Donoho, Johnstone, Kerkyacharian and Picard (“Density estimation by wavelet thresholding,” The Annals of Statistics, Vol. 24, No. 2, 1996, pp. 508-539). To show the lower ...
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