3 papers
stat.ME2026
Bayesian nonparametric models for zero-inflated count-compositional data using ensembles of regression trees
André F. B. Menezes, Andrew C. Parnell, Keefe Murphy
Count-compositional data arise in many different fields, including high-throughput sequencing experiments, ecological surveys, and palaeoclimate studies, where a common, important…
stat.ME2025
Finite mixture representations of zero-and--inflated distributions for count-compositional data
André F. B. Menezes, Andrew C. Parnell, Keefe Murphy
We provide novel probabilistic portrayals of two multivariate models designed to handle zero-inflation in count-compositional data. We develop a new unifying framework that represe…
stat.ME2024
Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data
Yong Chen Goh, Wuu Kuang Soh, Andrew C. Parnell +1
Dealing with missing data poses significant challenges in predictive analysis, often leading to biased conclusions when oversimplified assumptions about the missing data process ar…