This project implements and compares nonparametric estimators for convolution densities ψ = f ⋆ g, where f and g are unknown probability density functions. The implementation uses higher-order kernels ...

Abstract: A new multi-target tracking algorithm, termed as the Gaussian sum convolution probability hypothesis density (GSCPHD) filter, is proposed. The filter is calculated by a bank of convolution ...

Abstract: The probability hypothesis density (PHD) filter is a practical alternative to the optimal Bayesian multi-target filter based on random finite sets. It propagates the posterior intensity (or ...

This is a preview. Log in through your library . Abstract We study the problem of nonparametric estimation under 𝕃p-loss, p ∈ [1, ∞), in the framework of the convolution structure density model on ℝd ...

Several important multivariate probability inequalities can be formulated in terms of multivariate convolutions of the form ∫ $f_{1}(x)f_{2}(x-\theta )dx$, where ...

Measurement error arises when the observed data deviate from true values due to inaccuracies in measurement processes, potentially leading to biased estimates and ...