Bayesian autoregressive adaptive refined descriptive sampling algorithm in the Monte Carlo simulation
This paper deals with the Monte Carlo Simulation in a Bayesian framework. It shows the importance of the use of Monte Carlo experiments through refined descriptive sampling within the autoregressive model $ X_{t}=\rho X_{t-1}+Y_{t} $ , where $ 0 \lt \rho \lt 1 $ and the errors $ Y_{t} $ are independ...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-07-01
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| Series: | Statistical Theory and Related Fields |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24754269.2023.2180225 |
| Summary: | This paper deals with the Monte Carlo Simulation in a Bayesian framework. It shows the importance of the use of Monte Carlo experiments through refined descriptive sampling within the autoregressive model $ X_{t}=\rho X_{t-1}+Y_{t} $ , where $ 0 \lt \rho \lt 1 $ and the errors $ Y_{t} $ are independent random variables following an exponential distribution of parameter θ. To achieve this, a Bayesian Autoregressive Adaptive Refined Descriptive Sampling (B2ARDS) algorithm is proposed to estimate the parameters ρ and θ of such a model by a Bayesian method. We have used the same prior as the one already used by some authors, and computed their properties when the Normality error assumption is released to an exponential distribution. The results show that B2ARDS algorithm provides accurate and efficient point estimates. |
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| ISSN: | 2475-4269 2475-4277 |