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

ABSTRACT: Autoregressive (AR) models are useful in time series analysis. Inferences under such models are distorted in the presence of measurement error, a common feature in applications. 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 consider two measurement error models to describe different data contamination scenarios. We propose an estimating equation approach to estimate the AR 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 those related to asymptomatic cases and varying incubation periods. We implemen

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC10184652 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature