TIME-DISCRETIZATION APPROXIMATION ENRICHES CONTINUOUS-TIME DISCRETE-SPACE MODELS FOR ANIMAL MOVEMENT

Continuous time discrete state models are a valuable tool for explaining animal movement. However, data collection to fit such models over a spec-ified window of time can be misaligned with the actual realization of the movement process. This necessitates approximate model fitting, at present, through approximate imputation distributions (AIDs). Here, we propose a direct time-discretization approximation to the likelihood. The approach em-ploys familiar ideas from hidden Markov modeling. Computation is imple-mented through the induced infinitesimal generator matrix. Linearization of this matrix expedites computation time. Through simulation and a real data application involving whale movement, we demonstrate that this model fitting strategy can outperform AID approaches.

Duke Scholars

Author Alan E. Gelfand Statistical Science