Time series model with the autoregressive structure on clinical data | ||
| Journal of Statistical Modelling: Theory and Applications | ||
| دوره 6، شماره 1، فروردین 2025، صفحه 29-47 اصل مقاله (225.92 K) | ||
| نوع مقاله: Original Scientific Paper | ||
| شناسه دیجیتال (DOI): 10.22034/jsmta.2025.22923.1176 | ||
| نویسندگان | ||
| Mohammad Ghanemi؛ Zahra Khodadadi* ؛ Karim Zare | ||
| Department of Statistics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Autoregressive structure؛ Clinical data؛ Discontinuous time series model؛ Expectation/conditional maximisationeither algorithm | ||
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آمار تعداد مشاهده مقاله: 104 تعداد دریافت فایل اصل مقاله: 68 |
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