Bayesian Inference for Stochastic Cusp Catastrophe Model with Partially Observed Data

Ding-Geng Chen, Haipeng Gao and Chuanshu Ji
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Ding-Geng Chen: College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA
Haipeng Gao: Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599, USA
Chuanshu Ji: Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599, USA

Mathematics, 2021, vol. 9, issue 24, 1-9

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.

Keywords: cusp catastrophe model; stochastic differential equation; transition density; Bayesian inference; data augmentation; Hamiltonian Monte Carlo (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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