DETECTION OF POINTS OF CLIMATIC CHANGES, A BAYESIAN APPROACH IN CLIMATIC DATA OF THE CITY OF SÃO PAULO

Resumen

In this study, we introduce a statistical model applied to climate change data (annual mean temperature and annual mean rain precipitation for a long period) obtained from a climate station in São Paulo City Brazil. The assumed model used in the data analysis consists 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 fitted statistical model to get good predictions for annual mean temperature and annual mean rain precipitation and also to be used to identify the time of possible climate change-points.