Density estimation is a fundamental component in statistical analysis, aiming to infer the probability distribution of a random variable from a finite sample without imposing restrictive parametric ...
This paper develops nonparametric deconvolution density estimation over SO(N), the group of N × N orthogonal matrices of determinant 1. The methodology is to use the group and manifold structures to ...
© CBS Density estimation of complex data processes by means of neural networks and the integration of these networks in filter methods for the analysis of time ...
The performance of multivariate kernel density estimates depends crucially on the choice of bandwidth matrix, but progress towards developing good bandwidth matrix selectors has been relatively slow.
We retrospectively analyzed 1,080 nonactionable three-dimensional (3D) reconstructed DBT screening examinations acquired between 2011 and 2016. Reference tissue segmentations were generated using ...
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