Stochastic cusp catastrophe model and its Bayesian computations
| Autor: | Ding-Geng Chen, Haipeng Gao, Chuanshu Ji, Xinguang Chen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Cusp (singularity) 021103 operations research Economics Business & Finance Mathematics::Number Theory Bayesian probability 0211 other engineering and technologies 02 engineering and technology Bayesian inference Mathematics::Geometric Topology 01 natural sciences Hybrid Monte Carlo Physics::Popular Physics 010104 statistics & probability Stochastic differential equation Statistical inference Statistical physics 0101 mathematics Statistics Probability and Uncertainty Catastrophe theory Differential (mathematics) Mathematics |
| Zdroj: | J Appl Stat |
| ISSN: | 1360-0532 0266-4763 |
| DOI: | 10.1080/02664763.2021.1922993 |
| Popis: | This paper revitalizes the investigation of the classical cusp catastrophe model in catastrophe theory and tackles the unsolved statistical inference problem concerning stochastic cusp differential equation. This model is challenging because its associated transition density hence the likelihood function is analytically intractable. We propose a novel Bayesian approach combining Hamiltonian Monte Carlo with two likelihood approximation methods, namely, Euler approximation and Hermite expansion. We validate this novel approach through a series of simulation studies. We further demonstrate potential application of this novel approach using the real USD/EUR exchange rate. |
| Databáze: | OpenAIRE |
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