Bayesian inference for stochastic cusp catastrophe model with partially observed data
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Date
Authors
Chen, Ding-Geng (Din)
Gao, Haipeng
Ji, Chuanshu
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
The purpose of this paper is to develop a data augmentation technique for statistical
inference concerning stochastic cusp catastrophe model subject to missing data and partially observed
observations. We propose a Bayesian inference solution that naturally treats missing observations as
parameters and we validate this novel approach by conducting a series of Monte Carlo simulation
studies assuming the cusp catastrophe model as the underlying model. We demonstrate that this
Bayesian data augmentation technique can recover and estimate the underlying parameters from the
stochastic cusp catastrophe model.
Description
Keywords
Cusp catastrophe model, Stochastic differential equation, Transition density, Bayesian inference, Data augmentation, Hamiltonian Monte Carlo
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Citation
Chen, D.-G.; Gao, H.; Ji, C.
Bayesian Inference for Stochastic
Cusp Catastrophe Model with
Partially Observed Data. Mathematics
2021, 9, 3245. https://DOI.org/10.3390/math9243245.