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 Conference Paper Faculty Publications- Conference Papers 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 Estimating Function Approach for Modelling Non-Gaussian Time Series Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.257-271 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-4880-1_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-4880-1_20</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019299183?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019299183?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 and Data Science, CHRIST (Deemed to be University), Bengaluru, India; John N., Department of Statistics and Data Science, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource In this study, an effective technique for estimating parameters of autoregressive models with non-Gaussian errors has been presented. For non-Gaussian error distributions, classical techniques like maximum likelihood estimation (MLE) are found to be computationally intractable. To address this issue, an estimating function (EF) technique has been utilized, which effectively estimates the model parameters by taking advantage of the error structure. Specifically, AR(1) with logistic errors has been used, and the optimal estimating functions have been derived by constructing martingale-based estimating equations tailored to logistic errors. A hybrid AR(1)-ANN model has also been developed to integrate the strengths of both linear AR(1) and nonlinear ANN models. The robustness and efficiency of the pro-posed approach in parameter estimation have been investigated using simulations, comparing its mean squared error (MSE) and bias to those of MLE. The applicability of this approach has been further demonstrated using both simulated and real datasets. The results show that, in the presence of non-Gaussian errors, the EF method provides a computationally efficient and reliable alternative to classical estimation methodologies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource ANN; Autoregressive; Estimating function Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981964879-5; Language A language of the resource English Type The nature or genre of the resource Conference paper 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