Minimax Quasi-Bayesian Estimation in Sparse Canonical Correlation Analysis via a Rayleigh Quotient Function
提出一种拟贝叶斯估计方法，结合瑞利商函数和尖峰-平板先验，在稀疏典型相关分析中达到极小极大估计速率且计算高效，通过MCMC实现，在连续和截断数据上优于现有方法，并应用于Covid-19临床与蛋白质组学数据关联分析。
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.