@article {Rankin052266, author = {Robert W. Rankin}, title = {EM and component-wise boosting for Hidden Markov Models: a machine-learning approach to capture-recapture}, elocation-id = {052266}, year = {2016}, doi = {10.1101/052266}, publisher = {Cold Spring Harbor Laboratory}, 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.}, URL = {https://www.biorxiv.org/content/early/2016/12/14/052266}, eprint = {https://www.biorxiv.org/content/early/2016/12/14/052266.full.pdf}, journal = {bioRxiv} }