Robust and sparse gaussian graphical modelling under cell-wise contamination

Shota Katayama, Hironori Fujisawa, Mathias Drton

研究成果: Article査読

3 被引用数 (Scopus)

抄録

Graphical modelling explores dependences among a collection of variables by inferring a graph that encodes pairwise conditional independences. For jointly Gaussian variables, this translates into detecting the support of the precision matrix. Many modern applications feature high-dimensional and contaminated data that complicate this task. In particular, traditional robust methods that down-weight entire observation vectors are often inappropriate as high-dimensional data may feature partial contamination in many observations. We tackle this problem by giving a robust method for sparse precision matrix estimation based on the γ-divergence under a cell-wise contamination model. Simulation studies demonstrate that our procedure outperforms existing methods especially for highly contaminated data.

本文言語English
論文番号e181
ジャーナルStat
7
1
DOI
出版ステータスPublished - 2018
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 統計学、確率および不確実性

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