Efficient MCMC estimation of inflated beta regression models
Phillip Li ()
Computational Statistics, 2018, vol. 33, issue 1, No 5, 127-158
Abstract This paper introduces a new and computationally efficient Markov chain Monte Carlo (MCMC) estimation algorithm for the Bayesian analysis of zero, one, and zero and one inflated beta regression models. The algorithm is computationally efficient in the sense that it has low MCMC autocorrelations and computational time. A simulation study shows that the proposed algorithm outperforms the slice sampling and random walk Metropolis–Hastings algorithms in both small and large sample settings. An empirical illustration on a loss given default banking model demonstrates the usefulness of the proposed algorithm.
Keywords: Fractional response data; Proportions data; Loss given default; Data augmentation; Markov chain Monte Carlo; Pseudo likelihood (search for similar items in EconPapers)
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