This paper presents a significant advancement in the estimation of the Composite Link Model within a penalized likelihood framework, specifically designed 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 capability to capture fine-grained patterns while comparing its computational performance to the conventional algorithm. The proposed methodology offers notable improvements in computational speed, storage efficiency, and practical applicability, making it suitable for a wide range of fields where high-dimensional data are provided in grouped formats.

翻译：本文在惩罚似然框架下提出了复合链接模型估计方法的重要进展，该方法专门用于处理分组计数数据的间接观测问题。尽管该模型在此类场景中表现有效，但在大规模高维情境下的应用面临计算挑战。为克服这一难题，我们提出了一种重构的迭代估计流程，该流程利用广义线性阵列模型实现了多维数据中潜在分布的分解与平滑估计。通过在高维死亡率数据集上的应用，我们展示了该模型捕捉细粒度模式的能力，并将其计算性能与传统算法进行了对比。所提出的方法在计算速度、存储效率和实际适用性方面均有显著提升，适用于各类以分组形式提供高维数据的学科领域。