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

Robert William Rankin

doi:10.1101/052266

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bioRxiv · 10.1101/052266

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

Robert William Rankin

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Abstract

This study introduces statistical boosting for capture-mark-recapture (CMR) models. It is a shrinkage estimator that constrains the complexity of a CMR model in order to promote automatic variable-selection and avoid over-fitting. I discuss the philosophical similarities between boosting and AIC model-selection, and show through simulations that a boosted Cormack-Jolly-Seber model often out-performs AICc methods, in terms of estimating survival and abundance, yet yields qualitatively similar estimates. This new boosted CMR framework is highly extensible and could provide a rich, unified framework for addressing many topics in CMR, such as non-linear effects (splines and CART-like trees), individual-heterogeneity, and spatial components.

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Robert William Rankin. 2016-05-09. EM and component-wise boosting for Hidden Markov Models: a machine-learning approach to capture-recapture. https://doi.org/10.1101/052266

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