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2012 IEEE 12th International Conference on Data Mining Workshops
Bootstrap Confidence Intervals in DirectLiNGAM
Brussels, Belgium Belgium
December 10-December 10
ISBN: 978-1-4673-5164-5
We have been considering a problem of finding significant connection strengths of variables in a linear non-Gaussian causal model called LiNGAM. In our previous work, bootstrap confidence intervals of connection strengths were simultaneously computed in order to test their statistical significance. However, the distribution of estimated elements in an adjacency matrix obtained by the bootstrap method was not close enough to the real distribution even though the number of bootstrap replications was increased. Moreover, such a naive approach raised the multiple comparison problem which many directed edges were likely to be falsely found significant. In this study, we propose a new approach used to correct the distribution obtained by the bootstrap method. We also apply a representative technique of multiple comparison, the Bonferroni correction, then evaluate its performance. The result of this study shows that the new distribution is more stable and also even closer to the real distribution. Besides, the number of falsely found significant edges is less than the previous approach.
Index Terms:
Vectors,Niobium,Mathematical model,Adaptation models,Equations,Bayesian methods,Data models,bootstrap method,Structural equation models,Bayesian networks,non-Gaussianity,causal discovery,Bayesian information criteria,adaptive Lasso
Kittitat Thamvitayakul, Shohei Shimizu, Tsuyoshi Ueno, Takashi Washio, Tatsuya Tashiro, "Bootstrap Confidence Intervals in DirectLiNGAM," icdmw, pp.659-668, 2012 IEEE 12th International Conference on Data Mining Workshops, 2012
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