Department of Marine Geology, Faculty of Marine Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia.
Department of Statistics, Yazd University, P.O. Box 89175-741, Yazd, Iran.
Math Biosci Eng. 2023 Jan;20(2):2847-2873. doi: 10.3934/mbe.2023134. Epub 2022 Dec 1.
Statistical modeling and forecasting of time-to-events data are crucial in every applied sector. For the modeling and forecasting of such data sets, several statistical methods have been introduced and implemented. This paper has two aims, i.e., (i) statistical modeling and (ii) forecasting. For modeling time-to-events data, we introduce a new statistical model by combining the flexible Weibull model with the Z-family approach. The new model is called the Z flexible Weibull extension (Z-FWE) model, where the characterizations of the Z-FWE model are obtained. The maximum likelihood estimators of the Z-FWE distribution are obtained. The evaluation of the estimators of the Z-FWE model is assessed in a simulation study. The Z-FWE distribution is applied to analyze the mortality rate of COVID-19 patients. Finally, for forecasting the COVID-19 data set, we use machine learning (ML) techniques i.e., artificial neural network (ANN) and group method of data handling (GMDH) with the autoregressive integrated moving average model (ARIMA). Based on our findings, it is observed that ML techniques are more robust in terms of forecasting than the ARIMA model.
时间事件数据的统计建模和预测在各个应用领域都至关重要。对于此类数据集的建模和预测，已经引入并实施了几种统计方法。本文有两个目的，即（i）统计建模和（ii）预测。为了对时间事件数据进行建模，我们通过将灵活的 Weibull 模型与 Z 族方法相结合，引入了一种新的统计模型。该新模型称为 Z 灵活 Weibull 扩展（Z-FWE）模型，其中获得了 Z-FWE 模型的特征。获得了 Z-FWE 分布的最大似然估计量。在模拟研究中评估了 Z-FWE 模型估计量的评估。将 Z-FWE 分布应用于分析 COVID-19 患者的死亡率。最后，为了对 COVID-19 数据集进行预测，我们使用了机器学习（ML）技术，即人工神经网络（ANN）和数据处理组方法（GMDH）与自回归综合移动平均模型（ARIMA）相结合。根据我们的发现，观察到 ML 技术在预测方面比 ARIMA 模型更稳健。
