1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
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…
Predicting BVD Re-emergence in Irish Cattle From Highly Imbalanced Herd-Level Data Using Machine Learning Algorithms
Niamh Mimnagh, Andrew Parnell, Conor McAloon +7
Bovine Viral Diarrhoea (BVD) has been the focus of a successful eradication programme in Ireland, with the herd-level prevalence declining from 11.3% in 2013 to just 0.2% in 2023.…
Seemingly unrelated Bayesian additive regression trees for cost-effectiveness analyses in healthcare
Jonas Esser, Mateus Maia, Andrew C. Parnell +4
In recent years, theoretical results and simulation evidence have shown Bayesian additive regression trees to be a highly-effective method for nonparametric regression. Motivated b…
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…