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- Title
Bayesian Inference for Stochastic Cusp Catastrophe Model with Partially Observed Data.
- Authors
Chen, Ding-Geng; Gao, Haipeng; Ji, Chuanshu
- 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.
- Subjects
CATASTROPHE modeling; BAYESIAN field theory; MONTE Carlo method; MISSING data (Statistics); DATA augmentation
- Publication
Mathematics (2227-7390), 2021, Vol 9, Issue 24, p3245
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math9243245