Predicting the publication productivity of research groups is a basic task for academic administrators and funding agencies. However, it is an elusive task due to diversity in researchers’ productivity patterns. This study proposed a model for the dynamics of the productivity, inspired by the distribution feature of the number of a researcher's publications. It is a piecewise Poisson model, analyzing and predicting the publication productivity of researchers by piecewise regression. The principle of the model is built on the explanation for the distribution feature as a result of an inhomogeneous Poisson process that can be approximated as a piecewise Poisson process. The principle is validated by applying it on the high-quality dblp dataset. The effectiveness of the model is tested on the dataset by fine fittings on the distribution of the number of publications for researchers, the evolutionary trend of their publication productivity, and the probability of producing publications. The model has the advantage of providing results in an unbiased way; thus would be useful for funding agencies that evaluate a vast number of applications provided by research groups with a quantitative index on publications.

预测研究小组的出版效率是学术管理人员和资助机构的基本任务。但是，由于研究人员生产力模式的多样性，这是一项艰巨的任务。这项研究提出了一个生产率动态模型，该模型受研究人员出版物数量分布特征的启发。这是一个分段的Poisson模型，通过分段回归分析和预测研究人员的出版物生产率。该模型的原理基于对不均匀泊松过程的分布特征的解释，该泊松过程可以近似为分段泊松过程。通过将其应用于高质量dblp数据集来验证该原理。通过对研究人员的出版物数量分布，其出版物生产率的演变趋势以及产生出版物的可能性进行精细拟合，在数据集上测试了模型的有效性。该模型的优点是可以无偏见地提供结果。因此，对于评估研究小组提供的具有出版物定量指标的大量申请的资助机构而言，这将很有用。