Pedersen, Martin Wæver3; Thygesen, Uffe Høgsbro6; Baktoft, Henrik3; Madsen, Henrik6
1 Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark2 Department of Informatics and Mathematical Modeling, Technical University of Denmark3 National Institute of Aquatic Resources, Technical University of Denmark4 Section for Population Ecology and Genetics, National Institute of Aquatic Resources, Technical University of Denmark5 Section for Freshwater Fisheries Ecology, National Institute of Aquatic Resources, Technical University of Denmark6 Department of Applied Mathematics and Computer Science, Technical University of Denmark
A hierarchical framework for simultaneous analysis of multiple related individual datasets is presented. The approach is very similar to mixed effects modelling as known from statistical theory. The model used at the individual level is, in principle, irrelevant as long as a maximum likelihood estimate and its uncertainty (Hessian) can be computed. The individual model used in this text is a hidden Markov model. A simulation study concerning a two-dimensional biased random walk is examined to verify the consistency of the hierarchical estimation framework. In addition, a study based on acoustic telemetry data from pike illustrates how the framework can identify individuals that deviate from the remaining population.
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Technical University of Denmark, DTU Informatics, Building 321, 2010