EM and component-wise boosting for Hidden Markov Models

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 realizationsā€¦

Link to Full Article: EM and component-wise boosting for Hidden Markov Models

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