Improved communication systems, shrinking battery sizes and the price drop of tracking devices have led to an increasing availability of trajectory tracking data. These data are often analyzed to understand animals behavior using mixture-type model.
In this work, we propose a new model based on the logistic-normal process. Due to a new formalization and the way we specify the core- gionalization matrix of the associated multivariate Gaussian process, we show that our model, differently from other proposals, is invariant with respect to the choice of the reference element and of the order- ing of the components of the probability vectors. We estimate the model under a Bayesian framework, using an approximation of the Gaussian process needed to avoid impractical computational time.
We perform a simulation study with the aim of showing the ability of the model to retrieve the parameters used to simulate the data. The model is then applied to the real data where a wolf is observed before and after procreation. Results are easy to interpret, showing differences in the two phases.
Joint work with: Enrico Bibbona (Politecnico di Torino), Clara Grazian (Università di Pescara), Sara Mancinelli (università "Sapienza" di Roma)
28 Gennaio 2019 - Sala 34 ore 10.30
Dott. Gianluca Mastrantonio - Politecnico di Torino