Research interests
Preferential sampling
During my PhD, I developed a spatio-temporal hierarchical model based on fisheries data sources to map fish distribution. It feeds on massive catch declaration data and standardized fine-scale scientific survey data. It accounts for preferential sampling of the commercial data.
You’ll find some description of this approach in the following references:
Alglave et al. (2022) for a spatial version of the framework.
Alglave et al. (in press) for a spatio-temporal version of the framework.
Change of support
I also developped a statistical approach handling change of support for complex environmental data (zero-inflated data with long tails). It allows to combine aggregated massive data and fine scale survey data to map fish distribution.
- Alglave et al. (under review) for a model accounting for change of support.
Modelling mechanisms in hierarchical spatio-temporal models
During my postdoc, I have worked on the development of an hierarchical framework modelling population dynamics in space by integrating survey data and commercial catch data.
- See Olmos et al. (2023) for a spatio-temporal model representing population dynamics of the Snow Crab in the Eastern Bering Sea.
Multivariate analysis of spatial/spatio-temporal data
Massive data sources give access to huge amount of spatial information on long time series. Methods to decompose the spatio-temporal variability of the signal into simple spatial and temporal patterns have been developed for the past 50 years based on the EOF (Empirical Orthogonal Function) method. I am interested in the development of these methods, how these methods are related between each other and how new research avenues could be drawn from these methods.
