Yazd University Journal of Statistical Modelling: Theory and Applications 2676-7392 6 1 2025 01 01 Time series model with the autoregressive structure on clinical data 29 47 3913 10.22034/jsmta.2025.22923.1176 EN Mohammad Ghanemi Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran Zahra Khodadadi Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran 0000-0001-8242-762X Karim Zare Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran 0000-0002-6234-3303 Journal Article 2025 03 16 This research explores a time series model with an autoregressive structure applied to clinical data‎. ‎The motivation for selecting this topic is to develop a probabilistic model for nonlinear time series with interventions in clinical datasets‎. ‎By distinguishing between the phases before and after the intervention and analyzing the changes during the intervention‎, ‎the analysis yields a precisely estimated effect of the intervention‎. ‎The iterative scheme expectation/conditional maximisationeither algorithm is proposed for parameter estimation‎, ‎and the observed information matrix is derived analytically‎. ‎A key focus of data analysis is evaluating the robustness of the model's estimates and understanding how minor local disturbances influence the model‎. ‎The local impact of the model is thoroughly analyzed across three disturbance scenarios‎. ‎To assess the performance of the proposed methods‎, ‎simulated datasets are presented‎, ‎incorporating expectation/conditional maximisationeither estimates to demonstrate the robustness of estimates in the presence of influential outliers‎. ‎Finally‎, ‎the proposed method is successfully applied to model new COVID-19 time series cases in the Czech Republic‎. ‎Appropriate criteria confirm the applicability of the proposed process‎, ‎alongside the impact of diagnostic analysis. https://jsm.yazd.ac.ir/article_3913_29bd01e81d1b4fd52aec7b7fd2fd7c7f.pdf