A notable Gamma‐Lindley first‐order autoregressive process: An application to hydrological data

Abstract

A new Gamma-Lindley (GaL) first-order autoregressive process (AR) is introduced, called GaL-AR(1). The distribution for the GaL-AR(1) innovation process and some of its structural properties are derived, such as the Laplace transform for its bivariate distribution, the conditional variance and expected value, and the spectral and autocorrelation functions. We propose two different estimation procedures whose asymptotic properties are studied by Monte Carlo experiments. An application to actual data from hydrology is performed. Results point out that our process outperforms other six non-Gaussian AR(1) models that are well defined in the time series literature.

Generalized gamma ARMA process for synthetic aperture radar amplitude and intensity data

“…To account for this fact, one can take an unconditional or a conditional perspective. From an unconditional point of view, Mello et al (2022) have proposed the first‐order Gamma‐Lindley process for describing hydrology data. Li and Lu (2022) have worked with categorical time series to detect changes in precipitation.…”