Estimating Function Approach for Modelling Non-Gaussian Time Series

Title

Estimating Function Approach for Modelling Non-Gaussian Time Series

Creator

Thomas, Anna; John, Nimitha

Description

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.

Source

Lecture Notes in Networks and Systems;Volume;1354 LNNS;pp.257-271

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

ANN; Autoregressive; Estimating function

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981964879-5;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-4880-1_20

https://www.scopus.com/pages/publications/105019299183?origin=resultslist