Dear MARMAM, For those of you who study marked-animals, I would like to bring to your attention a pre-print manuscript about a new mark-recapture method rooted in machine-learning. A free pdf is available here: http://www.biorxiv.org/content/early/2016/05/09/052266.full.pdf
The manuscript proposes a new way to fit high-dimensional mark-recapture models, using ideas from the machine-learning community. It is an alternative "model parsimony" strategy to AICc model-averaging or model-selection. It is targeted at researchers who seek automatic variable selection, interaction detection, and model parsimony based on predictive-performance. Code and tutorial are available on Github. Rankin RW (2016) "EM and component-wise boosting for Hidden Markov Models: a machine-learning approach to capture-recapture". bioRxiv pre-print doi:10.1011/052266, URL:http://github.com/faraway1nspace/HMMboost ABSTRACT: This study presents a new boosting method for capture-recapture models, routed in predictive-performance and machine-learning. The regularization algorithm combines Expectation-Maximization and boosting to yield a type of multimodel inference, including automatic variable selection and control of model complexity. By analyzing simulations and a real dataset, this study shows the qualitatively similar estimates between AICc model-averaging and boosted capture-recapture for the CJS model. I discuss a number of benefits of boosting for capture-recapture, including: i) ability to fit non-linear patterns (regression-trees, splines); ii) sparser, simpler models that are less prone to over-fitting, singularities or boundary-value estimates than conventional methods; iii) an inference paradigm that is routed in predictive-performance and free of p-values or 95% confidence intervals; and v) estimates that are slightly biased, but are more stable over multiple realizations of the data. Finally, I discuss some philosophical considerations to help practitioners motivate the use of either prediction-optimal methods (AIC, boosting) or model-consistent methods. The boosted capture-recapture framework is highly extensible and could provide a rich, unified framework for addressing many topics in capture-recapture, such as spatial capture-recapture, individual heterogeneity, and non-linear effects. Thank you :) Rob Rankin PhD Candidate Cetacean Research Unit Murdoch University Western Australia -- "You could give Aristotle a tutorial. And you could thrill him to the core of his being ... Such is the privilege of living after Newton, Darwin, Einstein, Planck, Watson, Crick and their colleagues." -- Richard Dawkins
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