169 research outputs found

    Generalization of t-statistic

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    Open House, ISM in Tachikawa, 2012.6.15統計数理研究所オープンハウス(立川)、H24.6.15ポスター発

    Generalization of t statistic and AUC by considering heterogeneity in probability distributions

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    Open House, ISM in Tachikawa, 2015.6.19統計数理研究所オープンハウス(立川)、H27.6.19ポスター発

    Fishery stock assessment based on asymmetric logistic model

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    Open House, ISM in Tachikawa, 2014.6.13統計数理研究所オープンハウス(立川)、H26.6.13ポスター発

    Maximization of the partial area under the ROC curve

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    Open House, ISM in Tachikawa, 2010.7.9統計数理研究所オープンハウス(立川)、H22.7.9ポスター発

    Density estimation based on U-divergence

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    Open House, ISM in Tachikawa, 2011.7.14統計数理研究所オープンハウス(立川)、H23.7.14ポスター発

    Functional Boosting---海洋生態学データへの応用

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    Open House, ISM in Tachikawa, 2013.6.14統計数理研究所オープンハウス(立川)、H25.6.14ポスター発

    Robust Independent Component Analysis via Minimum Divergence Estimation

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    Independent component analysis (ICA) has been shown to be useful in many applications. However, most ICA methods are sensitive to data contamination and outliers. In this article we introduce a general minimum U-divergence framework for ICA, which covers some standard ICA methods as special cases. Within the U-family we further focus on the gamma-divergence due to its desirable property of super robustness, which gives the proposed method gamma-ICA. Statistical properties and technical conditions for the consistency of gamma-ICA are rigorously studied. In the limiting case, it leads to a necessary and sufficient condition for the consistency of MLE-ICA. This necessary and sufficient condition is weaker than the condition known in the literature. Since the parameter of interest in ICA is an orthogonal matrix, a geometrical algorithm based on gradient flows on special orthogonal group is introduced to implement gamma-ICA. Furthermore, a data-driven selection for the gamma value, which is critical to the achievement of gamma-ICA, is developed. The performance, especially the robustness, of gamma-ICA in comparison with standard ICA methods is demonstrated through experimental studies using simulated data and image data.Comment: 7 figure
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