Information geometry of modal linear regression

Keishi Sando, Shotaro Akaho, Noboru Murata, Hideitsu Hino*


研究成果: Article査読

3 被引用数 (Scopus)


Modal linear regression (MLR) is used for modeling the conditional mode of a response as a linear predictor of explanatory variables. It is an effective approach to dealing with response variables having a multimodal distribution or those contaminated by outliers. Because of the semiparametric nature of MLR, constructing a statistical model manifold is difficult with the conventional approach. To overcome this difficulty, we first consider the information geometric perspective of the modal expectation–maximization (EM) algorithm. Based on this perspective, model manifolds for MLR are constructed according to observations, and a data manifold is constructed based on the empirical distribution. In this paper, the em algorithm, which is a geometric formulation of the EM algorithm, of MLR is shown to be equivalent to the conventional EM algorithm of MLR. The robustness of the MLR model is also discussed in terms of the influence function and information geometry.

ジャーナルInformation Geometry
出版ステータスPublished - 2019 6月 1

ASJC Scopus subject areas

  • 幾何学とトポロジー
  • 統計学および確率
  • 応用数学
  • コンピュータ サイエンスの応用
  • 計算理論と計算数学


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