Long memory conditional random fields on regular lattices

Ferretti, Angela, Ippoliti, L, Valentini, P and Bhansali, RJ ORCID: 0000-0002-7322-8140 (2023) Long memory conditional random fields on regular lattices. ENVIRONMETRICS, 34 (5).

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Official URL: http://dx.doi.org/10.1002/env.2817

Abstract

<jats:title>Abstract</jats:title><jats:p>This paper draws its motivation from applications in geophysics, agricultural, and environmental sciences where empirical evidence of slow decay of correlations have been found for data observed on a regular lattice. Spatial ARFIMA models represent a widely used class of spatial models for analyzing such data. Here, we consider their generalization to conditional autoregressive fractional integrated moving average (CARFIMA) models, a larger class of long memory models which allows a wider range of correlation behavior. For this class we provide detailed descriptions of important representative models, make the necessary comparison with some other existing models, and discuss some important inferential and computational issues on estimation, simulation and long memory process approximation. Results from model fit comparison and predictive performance of CARFIMA models are also discussed through a statistical analysis of satellite land surface temperature data.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	CARFIMA models, conditional autoregression, Gauss-Markov random fields, long memory random fields, spatial processes
Divisions:	Faculty of Science and Engineering > School of Physical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	13 Jul 2023 13:24
Last Modified:	24 Aug 2023 00:22
DOI:	10.1002/env.2817
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3171685