PT - JOURNAL ARTICLE AU - Robert W. Rankin TI - EM and component-wise boosting for Hidden Markov Models: a machine-learning approach to capture-recapture AID - 10.1101/052266 DP - 2016 Jan 01 TA - bioRxiv PG - 052266 4099 - http://biorxiv.org/content/early/2016/12/14/052266.short 4100 - http://biorxiv.org/content/early/2016/12/14/052266.full AB - 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.