Popović Predrag M, Bakouch Hassan S, Ristić Miroslav M
Faculty of Civil Engineering and Architecture, University of Niš, Niš, Serbia.
Department of Mathematics, College of Science, Qassim University, Buraydah, Saudi Arabia.
J Appl Stat. 2024 Oct 26;52(6):1195-1218. doi: 10.1080/02664763.2024.2419495. eCollection 2025.
A new non-linear stationary process for time series of counts is introduced. The process is composed of the survival and innovation component. The survival component is based on the generalized zero-modified geometric thinning operator, where the innovation process figures in the survival component as well. A few probability distributions for the innovation process have been discussed, in order to adjust the model for observed series with the excess number of zeros. The conditional maximum likelihood and the conditional least squares methods are investigated for the estimation of the model parameters. The practical aspect of the model is presented on some real-life data sets, where we observe data with inflation as well as deflation of zeroes so we can notice how the model can be adjusted with the proper parameter selection.
引入了一种用于计数时间序列的新的非线性平稳过程。该过程由生存和创新成分组成。生存成分基于广义零修正几何稀疏算子，其中创新过程也包含在生存成分中。为了针对零值过多的观测序列调整模型，讨论了创新过程的几种概率分布。研究了用于估计模型参数的条件最大似然法和条件最小二乘法。在一些实际数据集上展示了该模型的实际应用，在这些数据集中我们观察到既有零值膨胀又有零值收缩的数据，从而可以注意到如何通过适当的参数选择来调整模型。
