论文标题
在自回归模型下使用COVID-19数据的敏感性分析，对犯错的时间序列数据数据
Sensitivity Analysis of Error-Contaminated Time Series Data under Autoregressive Models with Application of COVID-19 Data
论文作者
论文摘要
自回归（AR）模型是时间序列分析中有用的工具。在存在测量误差的情况下，这种模型下的推论会扭曲，这在实践中非常普遍。在本文中，如果忽略了测量误差效应，我们建立了分析结果，以量化AR模型中参数估计的偏差。我们提出了两个测量误差模型，以描述数据污染的不同过程。提出了一种估计方程方法，用于估计具有测量误差效应的模型参数。我们进一步讨论了使用建议的方法进行预测。我们的工作灵感来自Covid-19数据，由于多种原因包括无症状病例和不同的孵化期，这些数据受到了错误的污染。我们通过对加拿大四个人口最多的省份进行敏感性分析和对COVID-19的死亡率的敏感性分析和预测来实施我们提出的方法。结果表明，在参数估计和预测中，合并或不纳入测量误差效应会产生相当不同的结果。
Autoregressive (AR) models are useful tools in time series analysis. Inferences under such models are distorted in the presence of measurement error, which is very common in practice. In this article, we establish analytical results for quantifying the biases of the parameter estimation in AR models if the measurement error effects are neglected. We propose two measurement error models to describe different processes of data contamination. An estimating equation approach is proposed for the estimation of the model parameters with measurement error effects accounted for. We further discuss forecasting using the proposed method. Our work is inspired by COVID-19 data, which are error-contaminated due to multiple reasons including the asymptomatic cases and varying incubation periods. We implement our proposed method by conducting sensitivity analyses and forecasting of the mortality rate of COVID-19 over time for the four most populated provinces in Canada. The results suggest that incorporating or not incorporating measurement error effects yields rather different results for parameter estimation and forecasting.
