EM and component-wise boosting for Hidden Markov Models: a machine-learning approach to capture-recapture

1 This study presents a new boosting method for capture-recapture models, rooted in predictive2 performance and machine-learning. The regularization algorithm combines Expectation-Maximization and 3 boosting to yield a type of multimodel inference, including automatic variable selection and control of model 4 complexity. By analyzing simulations and a real dataset, this study shows the qualitatively similar estimates 5 between AICc model-averaging and boosted capture-recapture for the CJS model. I discuss a number of 6 benefits of boosting for capture-recapture, including: i) ability to fit non-linear patterns (regression-trees, 7 splines); ii) sparser, simpler models that are less prone to over-fitting, singularities or boundary-value esti8 mates than conventional methods; iii) an inference paradigm that is rooted in predictive-performance and 9 free of p-values or 95% confidence intervals; and v) estimates that are slightly biased, but are more stable over 10 multiple realizations of the data. Finally, I discuss some philosophical considerations to help practitioners 11 motivate the use of either prediction-optimal methods (AIC, boosting) or model-consistent methods. The 12 boosted capture-recapture framework is highly extensible and could provide a rich, unified framework for 13 addressing many topics in capture-recapture, such as spatial capture-recapture, individual heterogeneity, and 14 non-linear effects. 15 16