# Sensitivity analysis of error-contaminated time series data under autoregressive models with the application of COVID-19 data

@article{Zhang2022SensitivityAO, title={Sensitivity analysis of error-contaminated time series data under autoregressive models with the application of COVID-19 data}, author={Qihuang Zhang and Grace Y. Yi}, journal={Journal of Applied Statistics}, year={2022} }

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…

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