Frequency behaviour for multinomial counts of fisheries discards via a nested wavelet zero and N inflated binomial model

Abstract: In this paper we identify the changing frequency behaviour of multinomial counts of fish species discarded by vessels in the Irish Sea. We use a Bayesian hierarchical model which captures dynamic frequency changes via a shrinkage model applied to wavelet basis functions. Wavelets are known for capturing data features at different temporal scales; we use a recently-proposed shrinkage prior from the factor analysis literature so that features at the finest levels of detail exhibit the greatest shrinkage. Rather than using a multinomial distribution for monitoring the changes in discards over time, which can be slow to fit and inflexible, we use a nested zero-and-N inflated (ZaNI) binomial distribution which enables much faster computation with no obvious deterioration in model flexibility. Our results show that seasonal behaviour in these data are not regular and occur at different frequencies. We also show that the nested ZaNI binomial distribution is a good fit to multinomial count data of this sort when an informative nested structure is applied.