7 papers
Bandwidth rules for Dirichlet kernel density estimation on the simplex
Jiajin Xie, Eduardo García-Portugués, Yong Wang
Dirichlet kernels provide a natural tool for density estimation on the simplex, but data-driven bandwidth selection for them has been mostly limited to likelihood cross-validation…
Goodness-of-fit for distributions on metric spaces
Diego Serrano, Eduardo García-Portugués, Ingrid Van Keilegom
We propose a general goodness-of-fit framework for distributions on separable metric spaces. Under suitable identifiability conditions, probability distributions are characterized…
On Stein's test of uniformity on the hypersphere
Paul Axmann, Bruno Ebner, Eduardo García-Portugués
We propose a new test of uniformity on the hypersphere based on a Stein characterization associated with the Laplace-Beltrami operator. We identify a sufficient class of test funct…
Blessing of dimensionality in cross-validated bandwidth selection on the sphere
José E. Chacón, Eduardo García-Portugués, Andrea Meilán-Vila
We study the asymptotic behavior of least-squares cross-validation bandwidth selection in kernel density estimation on the -dimensional hypersphere, . We show that the…
On the spherical cardioid distribution and its goodness-of-fit
Eduardo García-Portugués
In this paper, we study the spherical cardioid distribution, a higher-dimensional and arbitrary-order generalization of the circular cardioid distribution. This distribution is rot…
Density estimation for compositional data using nonparametric mixtures
Jiajin Xie, Yong Wang, Eduardo García-Portugués
Compositional data, representing proportions constrained to the simplex, arise in diverse fields such as geosciences, ecology, genomics, and microbiome research. Existing nonparame…
Out-of-bag prediction balls for random forests in metric spaces
Diego Serrano, Eduardo García-Portugués
Statistical methods for metric spaces provide a general and versatile framework for analyzing complex data types. We introduce a novel approach for constructing confidence regions…