Missing value imputation (MVI) has been studied for several decades being the basic
solution method for incomplete dataset problems, specifically those where some data …

Recently, numerous studies have been conducted on Missing Value Imputation (MVI),
intending the primary solution scheme for the datasets containing one or more missing …

We present a new package in R implementing Bayesian additive regression trees (BART).
The package introduces many new features for data analysis using BART such as variable …

Proteomics data are often plagued with missingness issues. These missing values (MVs)
threaten the integrity of subsequent statistical analyses by reduction of statistical power …

This paper develops Bayesian econometric methods for posterior inference in non-
parametric mixed frequency VARs using additive regression trees. We argue that regression …

In many application settings, data have missing entries, which makes subsequent analyses
challenging. An abundant literature addresses missing values in an inferential framework …

Bayesian additive regression trees (BART) is a flexible prediction model/machine learning
approach that has gained widespread popularity in recent years. As BART becomes more …

Missing data is a common problem in real-world data sets and it is amongst the most
complex topics in computer science and many other research domains. The common ways …

We investigate the fairness concerns of training a machine learning model using data with
missing values. Even though there are a number of fairness intervention methods in the …

Individuals often respond differently to identical treatments, and characterizing such
variability in treatment response is an important aim in the practice of personalized …