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, ...

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 ...

Due to the ever increasing demands to drive up DC-DC power converter density, topologies that had previously been used only in high power applications are now finding their way into medium and lower ...