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Minimax Quasi-Bayesian Estimation in Sparse Canonical Correlation Analysis via a Rayleigh Quotient Function

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  • Qiuyun Zhu
  • Yves Atchadé

Abstract

Canonical correlation analysis (CCA) is a popular statistical technique for exploring relationships between datasets. In recent years, the estimation of sparse canonical vectors has emerged as an important but challenging variant of the CCA problem, with widespread applications. Unfortunately, existing rate-optimal estimators for sparse canonical vectors have high computational cost. We propose a quasi-Bayesian estimation procedure that not only achieves the minimax estimation rate, but also is easy to compute by Markov chain Monte Carlo (MCMC). The method builds on (Tan et al.) and uses a rescaled Rayleigh quotient function as the quasi-log-likelihood. However, unlike (Tan et al.), we adopt a Bayesian framework that combines this quasi-log-likelihood with a spike-and-slab prior to regularize the inference and promote sparsity. We investigate the empirical behavior of the proposed method on both continuous and truncated data, and we demonstrate that it outperforms several state-of-the-art methods. As an application, we use the proposed methodology to maximally correlate clinical variables and proteomic data for better understanding the Covid-19 disease. Supplementary materials for this article are available online.

Suggested Citation

  • Qiuyun Zhu & Yves Atchadé, 2024. "Minimax Quasi-Bayesian Estimation in Sparse Canonical Correlation Analysis via a Rayleigh Quotient Function," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(548), pages 2647-2657, October.
  • Handle: RePEc:taf:jnlasa:v:119:y:2024:i:548:p:2647-2657
    DOI: 10.1080/01621459.2023.2271199
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