Score-Driven Modeling of Spatio-Temporal Data.

ABSTRACT: A simultaneous autoregressive score-driven model with autoregressive disturbances is developed for spatio-temporal data that may exhibit heavy tails. The model specification rests on a signal plus noise decomposition of a spatially filtered process,where the signal can be approximated by a nonlinear function of the past variables and a set of explanatory variables, while the noise follows a multivariate Student-t distribution. The key feature of the model is that the dynamics of the space-time varying signal are driven by the score of the conditional likelihood function.When the distribution is heavy-tailed, the score provides a robust update of the space-time varying location. Consistency and asymptotic normality ofmaximum likelihood estimators are derived along with the stochastic proper

SUBMITTER: Gasperoni F 

PROVIDER: S-EPMC7614622 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature