Matrix variate logistic regression model with application to EEG data.

Logistic regression has been widely applied in the field of biomedical research for a long time. In some applications, the covariates of interest have a natural structure, such as that of a matrix, at the time of collection. The rows and columns of the covariate matrix then have certain physical meanings, and they must contain useful information regarding the response. If we simply stack the covariate matrix as a vector and fit a conventional logistic regression model, relevant information can be lost, and the problem of inefficiency will arise. Motivated from these reasons, we propose in this paper the matrix variate logistic (MV-logistic) regression model. The advantages of the MV-logistic regression model include the preservation of the inherent matrix structure of covariates and the parsimony of parameters needed. In the EEG Database Data Set, we successfully extract the structural effects of covariate matrix, and a high classification accuracy is achieved.

• Publication year

  2011

• Venue

  Biostatistics

• Publication date

  2011-05-11

• Fields of study

  Medicine, Computer Science, Mathematics

• Identifiers
  DOI 10.1093/biostatistics/kxs023arXiv 1105.2150PMID 22753784
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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