1. A Bayesian Hidden Semi-Markov Model with Covariate-Dependent State Duration Parameters for High-Frequency Environmental Data
- Author
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Rojas-Salazar, Shirley, Schliep, Erin M., Wikle, Christopher K., Stanley, Emily H., Carpenter, Stephen R., and Lottig, Noah R.
- Subjects
FOS: Computer and information sciences ,Applications (stat.AP) ,Statistics - Applications - Abstract
Environmental time series data observed at high frequencies can be studied with approaches such as hidden Markov and semi-Markov models (HMM and HSMM). HSMMs extend the HMM by explicitly modeling the time spent in each state. In a discrete-time HSMM, the duration in each state can be modeled with a zero-truncated Poisson distribution, where the duration parameter may be state-specific but constant in time. We extend the HSMM by allowing the state-specific duration parameters to vary in time and model them as a function of known covariates observed over a period of time leading up to a state transition. In addition, we propose a data subsampling approach given that high-frequency data can violate the conditional independence assumption of the HSMM. We apply the model to high-frequency data collected by an instrumented buoy in Lake Mendota. We model the phycocyanin concentration, which is used in aquatic systems to estimate the relative abundance of blue-green algae, and identify important time-varying effects associated with the duration in each state., Comment: arXiv admin note: substantial text overlap with arXiv:2010.10739
- Published
- 2021
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