著者

島村 徹平 井元 清哉 宮野 悟 Shimamura Teppei Imoto Seiya Miyano Satoru

雑誌

データマイニングと統計数理研究会(第 7 回)

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Statistical modeling based on vector autoregressive model has been considered as a promising tool to reconstruct large-scale gene networks from time course microarray data. However, it remains a challenging problem due to the small sample size and the high-dimensionality of time course microarray data. We present a novel regression-based modeling strategy with a new class of regularization, called recursive elastic net. Numerical simulations and real data analysis show that the proposed method outperforms other traditional methods.