Juvêncio Nobre

SAR Image Segmentation Based on Level Set Approach and {\cal G}_A^0 Model

This paper proposes an image segmentation method for synthetic aperture radar (SAR), exploring statistical properties of SAR data to characterize image regions. We consider {\cal G}_A^0 distribution parameters for SAR image segmentation, combined to the level set framework. The {\cal G}_A^0 distribution belongs to a class of {\cal G} distributions that have been successfully used to model different regions in amplitude SAR images for data modeling purpose. Such statistical data model is fundamental to deriving the energy functional to perform region mapping, which is input into our level set propagation numerical scheme that splits SAR images into homogeneous, heterogeneous, and extremely heterogeneous regions. Moreover, we introduce an assessment procedure based on stochastic distance and the {\cal G}_A^0 model to quantify the robustness and accuracy of our approach. Our results demonstrate the accuracy of the algorithms regarding experiments on synthetic and real SAR data.

Leverage analysis for linear mixed models

We consider a generalized leverage matrix useful for the identification of influential units and observations in linear mixed models and show how a decomposition of this matrix may be employed to identify high leverage points for both the marginal fitted values and the random effect component of the conditional fitted values. We illustrate the different uses of the two components of the decomposition with a simulated example as well as with a real data set.

Regression models for pretest/posttest data in blocks:

We consider regression models with no intercepts to analyse pretest/posttest data from a dental study conducted under an experimental design involving a blocked factorial structure with two within individual factors. The proposed models accommodate block effects, heteroscedasticity, nonlinear relations between pretest and posttest measures and repeated measures. We compare multiplicative lognormal and gamma models to additive normal models fitted via generalized linear models methodology for repeated measures. Alternatively, we consider standard linear mixed models methodology to fit lognormal models, an option that facilitates modelling the within subjects covariance structure.

SAR Image Segmentation With Rényi's Entropy

Synthetic aperture radar (SAR) image segmentation is an important task in image processing. However, classic segmentation techniques are inadequate due to the presence of speckle noise. In this paper, we present a methodology for SAR image segmentation that uses the matrix of Rényis entropy. This matrix arises from SAR data that follows the GA0 model, and here, it is an input to segmentation methods. For performance evaluation of the proposed methodology, we employ the error of segmentation, the cross-region fitting index, the Dice measure, as well as the rates of false positives and negatives. Tests have been performed on synthetic and real SAR images and the matrix of entropy has improved the results, regardless of the increase of the number of looks. The Otsus method of global thresholding produced good segmentation results when applied to this matrix.

U-tests for variance components in one-way random effects models

We consider a test for the hypothesis that the within-treatment variance component in a one-way random effects model is null. This test is based on a decomposition of a

The Harris Extended Exponential Distribution

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\delta -DOCA: Achieving Privacy in Data Streams.

-statistic. Its asymptotic null distribution is derived under the mild regularity condition that the second moment of the random effects and the fourth moment of the within-treatment errors are finite. Under the additional assumption that the fourth moment of the random effect is finite, we also derive the distribution of the proposed

Residual Analysis for Linear Mixed Models

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Cook's distance for generalized linear mixed models

-test statistic under a sequence of local alternative hypotheses. We report the results of a simulation study conducted to compare the performance of the

U-tests for variance components in linear mixed models

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