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

Keisuke Teramoto, Kei Hirose

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)530-546
Number of pages17
JournalInternational Journal for Numerical Methods in Engineering
Volume123
Issue number2
DOIs
Publication statusPublished - Jan 30 2022

All Science Journal Classification (ASJC) codes

  • Numerical Analysis
  • Engineering(all)
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Sparse multivariate regression with missing values and its application to the prediction of material properties'. Together they form a unique fingerprint.

Cite this