In this paper, we propose an ensemble method based on bagging and decision tree to resolve the problem of diagnosing out-of-control signals in multivariate statistical process control. To classify the out-of-control signals, we obtain a series of classifiers through ensemble learning on decision tree. Then we will integrate the classification results of multiple classifiers to determine the fin...

Meta-analysis is a standard statistical method used to combine the conclusions of individual studies that are related and the results of single study alone can not answered to deal with issues. The data are summarized by one or more outcome measure estimates along with their standard errors. The multivariate model and the variations between studies are not considered in most articles. Here we d...

Current clinical and research neuroimaging protocols acquire images using multiple modalities, for instance, T1, T2, diffusion tensor and cerebral blood flow magnetic resonance images (MRI). These multivariate datasets provide unique and often complementary anatomical and physiological information about the subject of interest. We present a method that uses fused multiple modality (scalar and t...

Reeb spaces, as well their discretized versions called Mappers, are common descriptors used in topological data analysis, with plenty of applications various fields science, such computational biology and visualization, among others. The stability quantification the rate convergence Mapper to space has been studied a lot recent works (Brown et al. CoRR. arXiv:1909.03488 , 2019; Carrière Oudot F...

In this work, we propose a surface fitting strategy based on a two-step model to remove noise from digital images. In the first step, we minimize the total variation energy functional of an image by using the projection gradient method in order to obtain the dual variable as the smoothed normal vector. In the second step, we try to find a surface as the recovered image to fit the smoothed norma...