169 research outputs found
Generalization of t-statistic
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
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
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
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
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
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
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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