Caamaño-Carrillo, C., Bevilacqua, M., López, C., & Morales-Oñate, V. (2024). Nearest neighbors weighted composite likelihood based on pairs for (non-)Gaussian massive spatial data with an application to Tukey-hh random fields estimation. Comput. Stat. Data Anal., 191, 107887.
Abstract: A highly scalable method for (non-)Gaussian random fields estimation is proposed. In particular, a novel (a) symmetric weight function based on nearest neighbors for the method of maximum weighted composite likelihood based on pairs (WCLP) is studied. The new weight function allows estimating massive (up to millions) spatial datasets and improves the statistical efficiency of the WCLP method using symmetric weights based on distances, as shown in the numerical examples. As an application of the proposed method, the estimation of a novel non-Gaussian random field named Tukey-hh random field that has flexible marginal distributions (possibly skewed and/or heavy-tailed) is considered. In an extensive simulation study the statistical efficiency of the proposed nearest neighbors WCLP method with respect to the WCLP method using weights based on distances is explored when estimating the parameters of the Tukey-hh random field. In the Gaussian case the proposed method is compared with the Vecchia approximation from computational and statistical viewpoints. Finally, the effectiveness of the proposed methodology is illustrated by estimating a large dataset of mean temperatures in South -America. The proposed methodology has been implemented in an open-source package for the R statistical environment.
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Mery, D., Riffo, V., Zscherpel, U., Mondragon, G., Lillo, I., Zuccar, I., et al. (2015). GDXray: The Database of X-ray Images for Nondestructive Testing. J. Nondestruct. Eval., 34(4), 12 pp.
Abstract: In this paper, we present a new dataset consisting of 19,407 X-ray images. The images are organized in a public database called GDXray that can be used free of charge, but for research and educational purposes only. The database includes five groups of X-ray images: castings, welds, baggage, natural objects and settings. Each group has several series, and each series several X-ray images. Most of the series are annotated or labeled. In such cases, the coordinates of the bounding boxes of the objects of interest or the labels of the images are available in standard text files. The size of GDXray is 3.5 GB and it can be downloaded from our website. We believe that GDXray represents a relevant contribution to the X-ray testing community. On the one hand, students, researchers and engineers can use these X-ray images to develop, test and evaluate image analysis and computer vision algorithms without purchasing expensive X-ray equipment. On the other hand, these images can be used as a benchmark in order to test and compare the performance of different approaches on the same data. Moreover, the database can be used in the training programs of human inspectors.
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