A nimble-based Bayesian MCMC framework recovers GLARMA parameters for Negative-Binomial, Beta and Gamma responses under weak-to-informative priors and varying persistence, including near stationarity boundaries.
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Bayes Estimation of GLARMA Models With Applications
A nimble-based Bayesian MCMC framework recovers GLARMA parameters for Negative-Binomial, Beta and Gamma responses under weak-to-informative priors and varying persistence, including near stationarity boundaries.