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Estimating parameters of hidden Markov models based on marked individuals: use of robust design data
Author(s) -
Kendall William L.,
White Gary C.,
Hines James E.,
Langtimm Catherine A.,
Yoshizaki Jun
Publication year - 2012
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/11-1538.1
Subject(s) - markov chain , computer science , markov model , hidden markov model , redundancy (engineering) , econometrics , sampling (signal processing) , statistics , markov process , imperfect , mathematics , machine learning , artificial intelligence , filter (signal processing) , computer vision , operating system , linguistics , philosophy
Development and use of multistate mark–recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last 20 years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected‐value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We have also implemented these models in program MARK. This general framework could also be used by practitioners to consider constrained models of particular interest, or to model the relationship between within‐primary‐period parameters (e.g., state structure) and between‐primary‐period parameters (e.g., state transition probabilities).