Regularizing priors for Bayesian VAR applications to large ecological datasets.

ABSTRACT: Using multi-species time series data has long been of interest for estimating inter-specific interactions with vector autoregressive models (VAR) and state space VAR models (VARSS); these methods are also described in the ecological literature as multivariate autoregressive models (MAR, MARSS). To date, most studies have used these approaches on relatively small food webs where the total number of interactions to be estimated is relatively small. However, as the number of species or functional groups increases, the length of the time series must also increase to provide enough degrees of freedom with which to estimate the pairwise interactions. To address this issue, we use Bayesian methods to explore the potential benefits of using regularized priors, such as Laplace and regularized horse

SUBMITTER: Ward EJ 

PROVIDER: S-EPMC9651052 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature