Sparse multivariate regression with missing values and its application to the prediction of material properties

Keisuke Teramoto, Kei Hirose

研究成果: ジャーナルへの寄稿学術誌査読

2 被引用数 (Scopus)

抄録

In the field of materials science and engineering, statistical analysis and machine learning techniques have recently been used to predict multiple material properties from an experimental design. These material properties correspond to response variables in the multivariate regression model. In this study, we conduct a penalized maximum likelihood procedure to estimate model parameters, including the regression coefficients and covariance matrix of response variables. In particular, we employ (Formula presented.) -regularization to achieve a sparse estimation of The regression coefficients and inverse covariance matrix of response variables. In some cases, there may be a relatively large number of missing values in the response variables, owing to the difficulty of collecting data on material properties. We therefore propose a method that incorporates a correlation structure among the response variables into a statistical model to improve the prediction accuracy under the situation with missing values. The expectation maximization algorithm is also constructed, which enables application to a dataset with missing values in the responses. We apply our proposed procedure to real data consisting of 22 material properties.

本文言語英語
ページ(範囲)530-546
ページ数17
ジャーナルInternational Journal for Numerical Methods in Engineering
123
2
DOI
出版ステータス出版済み - 1月 30 2022

!!!All Science Journal Classification (ASJC) codes

  • 数値解析
  • 工学一般
  • 応用数学

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