GARTFIMA process and its empirical spectral density based estimation
Year: 2023
Type: article
Abstract: In this article, we introduce a Gegenbauer autoregressive tempered fractionally integrated moving average process. We work on the spectral density and autocovariance function for the introduced process. The parameter estimation is done using the empirical spectral density with the help of the nonlinear least square technique and the Whittle likelihood estimation technique. The performance of the proposed estimation techniques is assessed on simulated data. Further, the introduced process is shown to better model the real-world data in comparison to other time series models.
Source: Journal of Applied Statistics
Authors: Niharika Bhootna, Arun Kumar
Institution: Indian Institute of Technology Ropar
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Ministry of Education, India
OpenAlex ID: W4386423488