Inference for Multivariate Interval Data: Bridging Frequentist and Bayesian Paradigms Inferencia para datos interv licos multivariados: un puente entre los paradigmas frecuentista y bayesiano

In recent years, the challenges posed by massive datasets have led researchers to explore aggregated representations, particularly interval-valued data, within the framework of symbolic data analysis. Although most recent researchapart from Samadi et al. (2024), who focused on the bivariate casehas primarily addressed parameter estimation in univariate settings, this paper extends these investigations to the general multivariate case for the rst time. We derive maximum likelihood (ML) estimators for the parameters and establish their asymptotic distributions. Additionally, we develop a theoretical Bayesian framework, previously con ned to the univariate setting, and extend it to multivariate interval-valued data. We provide a detailed exposition of the proposed estimators and conduct comparative performance analyses. Finally, we validate the eectiveness of our estimators through simulations and real-world data analysis.