Work

Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data

Year: 2024

Type: preprint

Abstract: Dealing with missing data poses significant challenges in predictive analysis, often leading to biased conclusions when oversimplified assumptions about the missing data process are made. In cases where the data are missing not at random (MNAR), jointly modeling the data and missing data indicators is essential. Motivated by a real data application with partially missing multivariate outcomes related to leaf photosynthetic traits and several environmental covariates, we propose two methods under a selection model framework for handling data with missingness in the response variables suitable for recovering various missingness mechanisms. Both approaches use a multivariate extension of Bayesian additive regression trees (BART) to flexibly model the outcomes. The first approach simultaneously uses a probit regression model to jointly model the missingness. In scenarios where the relationship between the missingness and the data is more complex or non-linear, we propose a second approach using a probit BART model to characterize the missing data process, thereby employing two BART models simultaneously. Both models also effectively handle ignorable covariate missingness. The efficacy of both models compared to existing missing data approaches is demonstrated through extensive simulations, in both univariate and multivariate settings, and through the aforementioned application to the leaf photosynthetic trait data.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2412.14946

Authors: Goh, Yong Chen, Soh, Wuu Kuang, Parnell, Andrew C., Murphy, Keefe

Institutions: National University of Ireland, Maynooth, National Botanic Gardens of Ireland, University College Dublin, Insight SFI Research Centre for Data Analytics

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Science Foundation Ireland, European Regional Development Fund

Awards: G1383882326, G1637072894, G2460442960, Integrating multidisciplinary geoscientific data into forecasting models to monitor and predict coastal change: Proof of concept in Dublin Bay., Next generation variety testing for improved cropping on European farmland, INSIGHT_Phase 2, Co-Centre for Climate + Biodiversity and Water, G434337789, G4370438778, G4383370283, SFI Centre for Research Training in Foundations of Data Science, G5098725405, G5734533708, G6146454469, G639259897, G7354596360, INSIGHT - Irelands Big Data and Analytics Research Centre, G8242245802, G8591373359

OpenAlex ID: W4405637088

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