Application of Flexible Regression Model for Count Data Based on Renewal Processes

As counting data is an indispensable part of data mining in the era of big data and artificial intelligence, the processing of its data is particularly important, and it has attracted more and more attention in practical application. In this article, we use the flexible regression model of the count data in the renewal process to compare the treatment of over-dispersed medical data with the Poisson regression model, and takes the negative binomial regression model as a reference. The results show that the renewal counting model has a good fitting effect on the over-dispersed data, and it also shows the limitations of the classical Poisson model. The data set is a study of adverse drug events in 117 patients affected by Crohn's disease (a chronic inflammatory disease of the intestines).