A data driven procedure for density estimation with some applications
References (35)
A data-based algorithm for choosing the window width when estimating the density at a point
Comput. Statist. Data Anal.
(1983)An improved data-based algorithm for choosing the window width when estimating the density at a point
Comput. Statist. Data Anal.
(1986)- et al.
Classifier design with Parzen windows
Pattern Recognition Artif. Intell.
(1988) On a histogram method of density estimation
Commun. Statist.
(1973)- et al.
Spline transformations
J. R. Statist. Soc. Ser. B
(1971) On the estimation of a probability density function and the mode
Ann. Math. Statist.
(1962)Estimation of a multivariate density
Ann. Inst. Statist. Math.
(1966)- et al.
A non-parametric estimate of a multivariate density function
Ann. Math. Statist.
(1965) - et al.
Optimization of k-nearest neighbor density estimates
IEEE Trans. Inform. Theory
(1973) Evaluation of an unknown distribution density from observations
Soviet Math.
(1962)
On the shortest spanning subtree of a graph and the travelling salesman problem
Rate of strong consistency of two non-parametric density estimators
Ann. Statist.
(1975)
Nonparametric roughness penalties for probability densities
Biometrika
(1971)
Estimation of probability density and distribution function
IEEE Trans. Inform. Theory
(1968)
Statistical Density Estimation—a Survey
(1978)
Nonparametric Functional Estimation
(1983)
On consistent estimation of classes in S2 in the context of cluster analysis
Cited by (7)
On bandwidth selection using minimal spanning tree for kernel density estimation
2016, Computational Statistics and Data AnalysisMultivariate online kernel density estimation with Gaussian kernels
2011, Pattern RecognitionCitation Excerpt :Non-parametric methods such as Parzen kernel density estimators (KDE) [4–6] alleviate this problem by treating each observation as a component in the mixture model. There have been several studies on how to efficiently estimate the bandwidth of each component (e.g., [7–12]) and to incorporate the measurement noise into the estimated bandwidths, e.g., [13]. Several researchers have recognized the drawbacks of using same bandwidth for all components.
Exploratory data analysis of evoked response single trials based on minimal spanning tree
2001, Clinical NeurophysiologyA novel multiseed nonhierarchical data clustering technique
1997, IEEE Transactions on Systems, Man, and Cybernetics, Part B: CyberneticsNonparametric density estimation based on self-organizing incremental neural network for large noisy data
2017, IEEE Transactions on Neural Networks and Learning SystemsOnline discriminative kernel density estimator with gaussian kernels
2014, IEEE Transactions on Cybernetics
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