arXiv subjects

Rosangela H. Loschi

Publications and source records attributed to Rosangela H. Loschi.

4 recordsLinked to original sources

arXiv2026

Directional Asymmetry in Edge BasedSpatial Models via a Skew Normal Prior

We introduce a skewed edge based spatial prior, named RENeGe sk that extends the Gaussian RENeGe framework by incorporating directional asymmetry through a skew normal distribution. Skewness is defined on the edge graph and propagated to the node space, aligning asymmetric behavior with transitions across neighboring regions rather than with marginal node effects. The model is formulated within the skew normal framework and employs identifiable hierarchical priors together with low rank parameterizations to ensure scalability. The skew normal's stochastic representation is considered to facilitate the computational implementation. Simulation studies show that RENeGe sk recovers compact, edge-aligned directional structure more accurately than symmetric Gaussian priors, while remaining competitive under irregular spatial patterns. An application to cancer incidence data in Southern Brazil illustrates how the proposed approach yields stable area-level estimates while preserving localized, directionally driven spatial variation.

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arXiv2022

Flexible Bayesian modelling in dichotomous item response theory using mixtures of skewed item curves

Most Item Response Theory (IRT) models for dichotomous responses are based on probit or logit link functions which assume a symmetric relationship between the probability of a correct response and the latent traits of individuals submitted to a test. This assumption restricts the use of those models to the case in which all items have a symmetric behaviour. On the other hand, asymmetric models proposed in the literature impose that all the items in a test have an asymmetric behaviour. This assumption is inappropriate for great part of the tests which are, in general, composed by both symmetric and asymmetric items. Furthermore, a straightforward extension of the existing models in the literature would require a prior selection of the items' symmetry/asymmetry status. This paper proposes a Bayesian IRT model that accounts for symmetric and asymmetric items in a flexible though parsimonious way. That is achieved by assigning a finite mixture prior to the skewness parameter, with one of the mixture components being a point-mass at zero. This allows for analyses under both model selection and model averaging approaches. Asymmetric item curves are designed through the centred skew normal distribution, which has a particularly appealing parametrisation in terms of parameter interpretation and computational efficiency. An efficient MCMC algorithm is proposed to perform Bayesian inference and its performance is investigated in some simulated examples. Finally, the proposed methodology is applied to a data set from a large scale educational exam in Brazil.

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arXiv2021

Multipartition model for multiple change point identification

Among the main goals in multiple change point problems are the estimation of the number and positions of the change points, as well as the regime structure in the clusters induced by those changes. The product partition model (PPM) is a widely used approach for the detection of multiple change points. The traditional PPM assumes that change points split the set of time points in random clusters that define a partition of the time axis. It is then typically assumed that sampling model parameter values within each of these blocks are identical. Because changes in different parameters of the observational model may occur at different times, the PPM thus fails to identify the parameters that experienced those changes. A similar problem may occur when detecting changes in multivariate time series. To solve this important limitation, we introduce a multipartition model to detect multiple change points occurring in several parameters at possibly different times. The proposed model assumes that the changes experienced by each parameter generate a different random partition of the time axis, which facilitates identifying which parameters have changed and when they do so. We discuss a partially collapsed Gibbs sampler scheme to implement posterior simulation under the proposed model. We apply the proposed model to identify multiple change points in Normal means and variances and evaluate the performance of the proposed model through Monte Carlo simulations and data illustrations. Its performance is compared with some previously proposed approaches for change point problems. These studies show that the proposed model is competitive and enriches the analysis of change point problems.

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arXiv2020

Multivariate Log-Skewed Distributions with normal kernel and their Applications

We introduce two classes of multivariate log skewed distributions with normal kernel: the log canonical fundamental skew-normal (log-CFUSN) and the log unified skew-normal (log-SUN). We also discuss some properties of the log-CFUSN family of distributions. These new classes of log-skewed distributions include the log-normal and multivariate log-skew normal families as particular cases. We discuss some issues related to Bayesian inference in the log-CFUSN family of distributions, mainly we focus on how to model the prior uncertainty about the skewing parameter. Based on the stochastic representation of the log-CFUSN family, we propose a data augmentation strategy for sampling from the posterior distributions. This proposed family is used to analyze the US national monthly precipitation data. We conclude that a high dimensional skewing function lead to a better model fit.

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