Rosangela Helena Loschi

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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)
This paper extends previous results for the classical product partition model applied to the identiÿcation 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 sampling(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)
Incipient fault detection Induction machine stator-winding Fuzzy clusters Bayesian analysis Metropolis–Hastings algorithm a b s t r a c t 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 two-step formulation.(More)
In this paper we analyze the fraction of non-disjunction in Meiosis I assuming reference (non-informative) priors. We consider Jeffreys's approach to built a non-informative prior (Jeffreys's prior) for the fraction of non-disjunction in Meiosis I. We prove that Jeffreys's prior is a proper distribution. We perform Monte Carlo studies in order to compare(More)
AMS 1991 subject classifications: 62H05 62E15 Keywords: Bayes Conjugacy Shape parameter Skewness Skew-normal distribution Regression model Robustness a b s t r a c t Classes of shape mixtures of independent and dependent multivariate skew-normal distributions are considered and some of their main properties are studied. If interpreted from a Bayesian point(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)
When performing analysis of spatial data, there is often the need to aggregate geographical areas into larger regions, a process called regionalization or spatially constrained clustering. These algorithms assume that the items to be clustered are non-stochastic, an assumption not held in many applications. In this work, we present a new probabilistic(More)