Modeling of non-Gaussian time series in the presence of measurement error
subject: Asymptotic property; Autoregressive; Estimating function; Logistic errors; Measurement error
description: Measurement error in time series refers to inaccuracies or deviations in recorded data that can distort the true underlying patterns, potentially leading to biased estimates and misleading conclusions in statistical analysis. AR models with non-Gaussian errors are crucial for accurately capturing real-world time series data with skewness, heavy tails, or outliers, leading to better forecasts and more reliable statistical inferences. Our focus in this study is on an optimal estimating function (EF) method that accounts for measurement error while estimating the parameters of AR(1) with logistic innovation. The asymptotic properties of the obtained optimal EF are also proved. The simulation study shows that incorporating measurement error into the model yields better estimates compared to ignoring its presence when measurement error exists. This shows that ignoring the measurement error leads to biased estimates. 2026 Taylor & Francis Group, LLC.
creator: Thomas, Anna; John, Nimitha
source: Communications in Statistics: Simulation and Computation;
publisher: Taylor and Francis Ltd.
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 3610918;
format: online
language: English
type: Article
coverage: Thomas A., Department of Statistics & Data Science, CHRIST(Deemed to be University), Bengaluru, India; John N., Department of Statistics & Data Science, CHRIST(Deemed to be University), Bengaluru, India