Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Thomas, Anna; John, Nimitha Title A name given to the resource Modeling of non-Gaussian time series in the presence of measurement error Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Statistics: Simulation and Computation; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/03610918.2026.2625231" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/03610918.2026.2625231</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030248428?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030248428?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Thomas A., Department of Statistics &amp; Data Science, CHRIST(Deemed to be University), Bengaluru, India; John N., Department of Statistics &amp; Data Science, CHRIST(Deemed to be University), Bengaluru, India Description An account of the resource 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 &amp; Francis Group, LLC. Subject The topic of the resource Asymptotic property; Autoregressive; Estimating function; Logistic errors; Measurement error Publisher An entity responsible for making the resource available Taylor and Francis Ltd. Relation A related resource ISSN: 3610918; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online