Resumen de Time series with multiple change points and censored observations
René Castro Montoya, Gabriel Arcángel Rodríguez Yam, Felipe de Jesús Peraza Garay, José Vidal Jiménez Ramírez
This article examines a Bayesian model for a nonstationary time series with an unknown number of change points and censored observations. Each segment is assumed to be an autoregressive process with order one. To estimate the number and locations of change points, we use the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm. The censored problem is solved by imputing the censored values from a multivariate normal distribution based on the observed part. A numerical example shows that the estimates of the number of change points and their localizations have little bias. Additionally, the estimates are robust to the censoring percentage.
