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Individual judgments are sought in order to elicit values of the entries of pairwise comparison matrices in analytic hierarchy process. Some of these matrices are more consistent than others. But throwing out inconsistent matrices reduces the number of matrices. In this article, we propose a resampling technique to generate sets of pairwise comparison matrices, which pass the consistency check. The advantage of the resampling technique is that one can generate as many sets of pairwise comparison matrices as needed to select the ones that satisfy the consistency requirement. Based on these selected matrices, the priority weights of the alternatives are then estimated. We propose rank‐based statistical procedures to check the significance in the difference between estimated priorities of the alternatives to establish their most significant rank order. [ABSTRACT FROM AUTHOR]

Seam carving is a popular content-aware image resizing technique by removing unnoticeable seams with low energies for aesthetic purpose. However, it might also be used for malicious forgeries such as object removal. In this paper, a blind forensics approach is proposed to detect resized images by seam carving. Since seam carving mainly changes local textures, two excellent texture descriptors including Weber Local Descriptor (WLD) and Local Binary Patterns (LBP) are exploited for seam carving forgery detection. Specifically, the histogram features of WLD and LBP are extracted from candidate images, respectively. Then, Kruskal–Wallis statistic is exploited to select a subset of more discriminative features. Finally, support vector machine (SVM) is exploited as classifier to judge whether an image is original or suffered from seam carving. Extensive experiment results on a large set of test images show that the proposed approach achieves better performance than the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A general form of formula is presented for the rainfall Intensity-Duration-Frequency (IDF) relationship. This formula is derived from the nearly normal probability distribution function of transformed intensities. In order to transform the raw intensities, a correcting non-constant spread technique, the Kruskal-Wallis statistic, and the Box-Cox transformation are adopted. These transformations enable to express a simpler model for the IDF formula that agrees well with traditional IDF relationships. Since the proposed method allows the estimation of any percentile value of intensities with a single equation, the intensity percentile at arbitrary duration can be generated easily. The validity of the formula derived by means of the proposed method is assessed using data from major weather stations in Korea. The results show that the percentile intensities produced using the proposed method are in good agreement with those of traditional frequency analysis. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We present new algorithms for computing the exact distributions and p-values of quadratic t-sample distribution-free statistics of Kruskal–Wallis type. These algorithms are presented in terms of generating functions. We show that our algorithm also works for cases with ties and that it is much faster than existing algorithms. Moreover, we show how to use the results for the Kruskal–Wallis type statistics to compute the exact null distribution of the Chacko–Shorack statistic. [Copyright &y& Elsevier]