ArticleJournal of the American Statistical Association2021
Optimal Estimation of Wasserstein Distance on A Tree with An Application to Microbiome Studies.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Demographic distribution matching between real-world and virtual phantom population.Medical physics · 2026Article
- Phylogenetic association analysis with conditional rank correlation.Biometrika · 2024Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Compositional Data Analysis using Kernels in mass cytometry data.Bioinformatics advances · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The weighted UniFrac distance, a plug-in estimator of the Wasserstein distance of read counts on a tree, has been widely used to measure the microbial community difference in microbiome studies. Our investigation however shows that such a plug-in estimator, although intuitive and commonly used in practice, suffers from potential bias. Motivated by this finding, we study the problem of optimal estimation of the Wasserstein distance between two distributions on a tree from the sampled data in the high-dimensional setting. The minimax rate of convergence is established. To overcome the bias problem, we introduce a new estimator, referred to as the moment-screening estimator on a tree (MET), by using implicit best polynomial approximation that incorporates the tree structure. The new estimator is computationally efficient and is shown to be minimax rate-optimal. Numerical studies using both simulated and real biological datasets demonstrate the practical merits of MET, including reduced biases and statistically more significant differences in microbiome between the inactive Crohn's disease patients and the normal controls.
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