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Bayesian inference for multivariate probit model with latent envelope.

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  • Author(s): Lee K;Lee K; Park Y; Park Y
  • Source:
    Biometrics [Biometrics] 2024 Jul 01; Vol. 80 (3).
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
    • Publication Information:
      Publication: March 2024- : [Oxford] : Oxford University Press
      Original Publication: Alexandria Va : Biometric Society
    • Subject Terms:
      Bayes Theorem* ; Computer Simulation* ; Models, Statistical*; Multivariate Analysis ; Humans ; Linear Models ; Biometry/methods ; Normal Distribution
    • Abstract:
      The response envelope model proposed by Cook et al. (2010) is an efficient method to estimate the regression coefficient under the context of the multivariate linear regression model. It improves estimation efficiency by identifying material and immaterial parts of responses and removing the immaterial variation. The response envelope model has been investigated only for continuous response variables. In this paper, we propose the multivariate probit model with latent envelope, in short, the probit envelope model, as a response envelope model for multivariate binary response variables. The probit envelope model takes into account relations between Gaussian latent variables of the multivariate probit model by using the idea of the response envelope model. We address the identifiability of the probit envelope model by employing the essential identifiability concept and suggest a Bayesian method for the parameter estimation. We illustrate the probit envelope model via simulation studies and real-data analysis. The simulation studies show that the probit envelope model has the potential to gain efficiency in estimation compared to the multivariate probit model. The real data analysis shows that the probit envelope model is useful for multi-label classification.
      (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)
    • Grant Information:
      RS-2023-00211979 National Research Foundation of Korea; Office of the Vice Chancellor for Research and Graduate Education, University of Wisconsin-Madison
    • Contributed Indexing:
      Keywords: Bayesian inference; cell line data analysis; envelope model; multi-label classification; multivariate probit model
    • Publication Date:
      Date Created: 20240701 Date Completed: 20240701 Latest Revision: 20240701
    • Publication Date:
      20250114
    • Accession Number:
      10.1093/biomtc/ujae059
    • Accession Number:
      38949889