| Autor: |
Jorge Alberto Achcar, Emerson Barili |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
International Journal of Environment and Climate Change. 13:23-47 |
| ISSN: |
2581-8627 |
| DOI: |
10.9734/ijecc/2023/v13i61795 |
| Popis: |
In this study, we introduce a statistical model applied to climate change data consisting of an autoregressive times series (AR) model which represents a type of random process. A Bayesian approach using MCMC (Markov Chain Monte Carlo) methods is considered to get the inferences of interest. The main goal of the study is to have a model to get good predictions for mean temperature and also good to identify the time of possible change-points that might be present in the time series which could indicate the possible beginning of a change in climate. Applications of the proposed model are considered using annual average temperatures in some locations obtained over a period of time ranging from the end of 1800’s to a popular Bayesian discrimination criterion using MCMC methods.In addition to a good fit of the proposed model for the data, the model also was used to detect the times of climate changes in the different climate stations using CUSUM methodology. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
