Density estimation is a building block for many other statistical methods, such as
classification, nonparametric testing, and data compression. In this paper, we focus on a non …

We consider statistical inference in the density estimation model using a tree–based
Bayesian approach, with Optional Pólya trees as prior distribution. We derive near-optimal …

The estimation of probability densities based on available data is a central task in many
statistical applications. Especially in the case of large ensembles with many samples or high …

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to
generate multiple CyTOF samples in a single study, with each sample containing single-cell …

We study a non-parametric approach to multivariate density estimation. The estimators are
piecewise constant density functions supported by binary partitions. The partition of the …

With recent developments of data technology in biomedicine, factor data such as diagnosis
codes and genomic features, which can have tens to hundreds of discrete and unorderable …

Recently, distribution element trees (DETs) were introduced as an accurate and
computationally efficient method for density estimation. In this work, we demonstrate that the …

Modern data analysis provides scientists with statistical and machine learning algorithms
with impressive performance. In front of their extensive use to tackle problems of constantly …

Nonparametric density estimation is a fundamental task in statistics. In many applications,
such as clustering, non-parametric testing, classi? cation, anomaly detection and topological …

Density estimation is a fundamental problem in statistics. It is a building block for other
statistical methods, such as classification, nonparametric testing, and data compression. In …