Censored multivariate linear regression model - Pereira, Isabel (2023)
Often, real life problems require modelling several response variables together. This work analyses multivariate linear regression model when the data are censored. Censoring distorts the correlation structure of the underlying variables and increases the bias of the usual estimators. Thus, we propose three methods to deal with multivariate data under left censoring, namely, Expectation Maximization (EM), Data Augmen- tation (DA) and Gibbs Sampler with Data Augmentation (GDA). Re- sults from a simulation study show that both, DA and GDA estimates are consistent for low and moderate correlation.