GARTFIMA process and its empirical spectral density based estimation.

Bhootna, Niharika; Kumar, Arun

Bhootna, Niharika; Kumar, Arun.

Afiliación

Bhootna N; Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India.
Kumar A; Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, Punjab, India.

J Appl Stat ; 51(10): 1919-1945, 2024.

Article en En | MEDLINE | ID: mdl-39071254

ABSTRACT

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.

Palabras clave

62M10; Fractional ARIMA processes; Gegenbauer processes; parameter estimation; spectral density; tempered fractional ARIMA processes

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Appl Stat Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Reino Unido

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