On the accuracy of binned kernel density estimators
Journal of Multivariate Analysis
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Anisotropic point set surfaces
AFRIGRAPH '06 Proceedings of the 4th international conference on Computer graphics, virtual reality, visualisation and interaction in Africa
High-dimensional statistical measure for region-of-interest tracking
IEEE Transactions on Image Processing
Boundary kernels for adaptive density estimators on regions with irregular boundaries
Journal of Multivariate Analysis
Nonparametric density estimation with adaptive, anisotropic kernels for human motion tracking
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Robotics and Computer-Integrated Manufacturing
Kernel bandwidth optimization in spike rate estimation
Journal of Computational Neuroscience
Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions
Computational Statistics & Data Analysis
Local spatial co-occurrence for background subtraction via adaptive binned kernel estimation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Bayesian predictive kernel discriminant analysis
Pattern Recognition Letters
Nonparametric estimation of multivariate elliptic densities via finite mixture sieves
Journal of Multivariate Analysis
Bayesian estimation of adaptive bandwidth matrices in multivariate kernel density estimation
Computational Statistics & Data Analysis
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Multivariate versions of variable bandwidth kernel density estimators can lead to improvement over kernel density estimators using global bandwidth choices. These estimators are more flexible and better able to model complex (multimodal) densities. In this work, two variable bandwidth estimators are discussed: the balloon estimator which varies the smoothing matrix with each estimation point and the sample point estimator which uses a different smoothing matrix for each data point. A binned version of the sample point estimator is developed that, for various situations in low to moderate dimensions, exhibits less error (MISE) than the fixed bandwidth estimator and the balloon estimator. A practical implementation of the sample point estimator is shown through simulation and example to do a better job at reconstructing features of the underlying density than fixed bandwidth estimators. Computational details, including parameterization of the smoothing matrix, are discussed throughout.