Dirichlet kernel density estimation on the simplex with missing data

TL;DR

This paper introduces a novel Dirichlet kernel density estimator for compositional data on the simplex with missing data, using inverse probability weighting and adaptive kernels, with theoretical properties and practical applications demonstrated.

Contribution

It proposes a new nonparametric density estimator that handles missing data directly on the simplex without imputation, improving performance over existing methods.

Findings

Estimator has favorable boundary behavior and nonnegativity.

Simulation results show improved accuracy over existing methods.

Application to NHANES data identifies the modal immune profile.

Abstract

Nonparametric density estimation for compositional data supported on the simplex is examined under a missing at random mechanism. Rather than imputing missing values and estimating the density from a completed data set, we adopt a strategy based on inverse probability weighting. The proposed estimator uses an adaptive Dirichlet kernel, which ensures nonnegativity on the simplex and favorable behavior near the boundary. When the observation probabilities are unknown, they are estimated through a Nadaraya-Watson regression step. The large-sample properties of the estimator are derived, including pointwise bias and variance expansions, optimal smoothing rates, and asymptotic normality. A simulation study investigates its finite-sample performance under varying sample sizes and missing rates. Simulations show our method outperforms inverse-probability-weighted kernel density estimators based on additive and isometric log-ratio transformations of the data for certain target densities. The methodology is further illustrated through an application to leukocyte composition data from the National Health and Nutrition Examination Survey (NHANES), which allows for the identification of the modal immune profile in the sampled population.