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 ...
Nonparametric methods provide a flexible framework for estimating the probability density function of random variables without imposing a strict parametric model. By relying directly on observed data, ...
© 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 a kernel density estimator depends crucially on the size of its smoothing bandwidth. A data-driven bandwidth selector for density estimation at a point is proposed in this paper.
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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