Communication-efficient distributed statistical inference on zero-inflated Poisson models

Zero-inflated count outcomes are common in many studies, such as counting claim frequency in the insurance industry in which identifying and understanding excessive zeros are of interest. Moreover, with the progress of data collecting and storage techniques, the amount of data is too massive to be stored or processed by a single node or branch. Hence, to develop distributed data analysis is blossoming. In this paper, several communication-efficient distributed zero-inflated Poisson regression algorithms are developed to analyse such kind of large-scale zero-inflated data. Both asymptotic properties of the proposed estimators and algorithm complexities are well studied and conducted. Various simulation studies demonstrate that our proposed method and algorithm work well and efficiently. Finally, in the case study, we apply our proposed algorithms to car insurance data from Kaggle.