In this paper, we introduce a Goodness-of-Fit test for the Multivariate Exponential Power (MEP) distribution, a multivariate extension of the Generalized Gaussian, which has recently gained considerable interest as a model for wavelet coefficients in the context of color image retrieval and spread-spectrum watermarking. We present a size and power study of this test and show Goodness-of-Fit res...

For any generalized linear model, the Pearson goodness of fit statistic is the score test statistic for testing the current model against the saturated model. The relationship between the Pearson statistic and the residual deviance is therefore the relationship between the score test and the likelihood ratio test statistics, and this clarifies the role of the Pearson statistic in generalized li...

The severe “shuttle effect” of dissolved polysulfide intermediates and the poor electronic conductivity sulfur cathodes cause capacity decay lithium–sulfur batteries impede their commercialization. Herein, we synthesized a series well-designed yolk-shelled Fe3O4@carbon (YS-Fe3O4@C) nanocavities with different proportions Fe3O4 as efficient hosts to stabilize intermediates. nanocavity architectu...

In this paper, we propose ”stable marriages” algorithms based on a novel representation called marriage table. After explaining how properties as global satisfaction, sex equality and stability show in the representation, we define 3 algorithms corresponding to 3 different scans of the marriage table to meet progressively all constraints. The performance is evaluated in front of the population ...

Total variance is a statistical tool developed for improved estimates of frequency stability at averaging times up to half the test duration. As a descriptive statistic, Total variance performs an exact decomposition of the sample variance of the frequency residuals into components associated with descending frequency octaves. As an estimator of Allan variance, Total variance has modest bias an...

The use of artificial intelligence techniques, with the increase data generation capacity and advancement computational resources, has enabled industries to develop improve products without compromising laboratory industrial resources. In this paper, a supervised machine learning (ML) based technique was used predict yield strength (YS), ultimate tensile (UTS), elongation (EL) dual phase steels...