Fast Estimation of the Composite Link Model for Multidimensional Grouped Counts

Overview & Synopsis

This paper introduces a new approach for estimating the Composite Link Model within a penalized likelihood framework, aimed to address indirect observations of grouped count data. While the model is effective in these contexts, its application becomes computationally challenging in large, high-dimensional settings. To overcome this, we propose a reformulated iterative estimation procedure that leverages Generalized Linear Array Models, enabling the disaggregation and smooth estimation of latent distributions in multidimensional data. Through applications to high-dimensional mortality datasets, we demonstrate the model’s ability to capture fine-grained patterns while comparing its computational performance to the conventional algorithm.