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Activity Number: 458 - Models for Spatial and Environmental Data
Type: Contributed
Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics and the Environment
Abstract #312966
Title: A Bayesian Hidden Semi-Markov Model with Covariate-Dependent State Duration Intensities
Author(s): Shirley Rojas Salazar* and Erin Schliep and Christopher Wikle
Companies: University of Missouri and University of Missouri and University of Missouri
Keywords: HSMM; explicit duration HMM; MCMC
Abstract:

Hidden semi-Markov models (HSMMs) extend the hidden Markov model (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 intensity may be state-specific but constant in time. We extend the HSMM by allowing the state-specific intensities to vary in time and model them as a function of known covariates. In particular, the intensity at each state transition is a function of covariates observed over a period of time leading up to the transition. Model inference is obtained in Bayesian framework and our HSMM with time-varying intensities can be applied broadly in applications ranging from environmental, to speech recognition, to sports.


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