In this article, we propose several methodologies for handling missing or incomplete data in archetype analysis (AA) and archetypoid analysis (ADA). AA seeks to find archetypes, which are convex combi
We consider estimating the conditional prevalence of a disease from data pooled according to the group testing mechanism. Consistent estimators have been proposed in the literature, but they rely on t
Modern high-dimensional statistical inference often faces the problem of missing data. In recent decades, many studies have focused on this topic and provided strategies including complete-sample anal
Missing data are a common feature of micro-level transaction data used to construct hedonic real estate price indices. Missingness typically occurs in the descriptive characteristics required for qual
