Rosangela Helena Loschi

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This paper extends previous results for the classical product partition model (PPM) applied to the identification of multiple change points in the means and variances of time series. Prior distributions for these two parameters and for the probability p that a change takes place at a particular period of time are considered and a new scheme based on Gibbs(More)
The well-known product partition model (PPM) is considered for the identiÿcation of multiple change points in the means and variances of normal data sequences. In a natural fashion, the PPM may provide product estimates of these parameters at each instant of time, as well as the posterior distributions of the partitions and the number of change points.(More)
The multiple change point identification problem may be encountered in many subject areas, including disease mapping, medical diagnosis, industrial control, and finance. One appealing way of tackling the problem is through the product partition model (PPM), a Bayesian approach. Nowadays, practical applications of Bayesian methods have attracted attention(More)
—In this paper the incipient fault detection problem in induction machine stator-winding is considered. The problem is solved using a new technique of change point detection in time series, based on a three-step formulation. The technique can detect up to two change points in the time series. The first step consists of a Kohonen neural network(More)
In change point problems in general we should answer three questions: how many changes are there? Where are they? And, what is the distribution of the data within the blocks? In this paper, we develop a new full predictivistic approach for mod-eling observations within the same block of observation and consider the product partition model (PPM) for treating(More)