Annals of Statistics
- Ann. Statist.
- Volume 47, Number 6 (2019), 3413-3437.
Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors
In this paper we study the high-dimensional sparse directed acyclic graph (DAG) models under the empirical sparse Cholesky prior. Among our results, strong model selection consistency or graph selection consistency is obtained under more general conditions than those in the existing literature. Compared to Cao, Khare and Ghosh [Ann. Statist. (2019) 47 319–348], the required conditions are weakened in terms of the dimensionality, sparsity and lower bound of the nonzero elements in the Cholesky factor. Furthermore, our result does not require the irrepresentable condition, which is necessary for Lasso-type methods. We also derive the posterior convergence rates for precision matrices and Cholesky factors with respect to various matrix norms. The obtained posterior convergence rates are the fastest among those of the existing Bayesian approaches. In particular, we prove that our posterior convergence rates for Cholesky factors are the minimax or at least nearly minimax depending on the relative size of true sparseness for the entire dimension. The simulation study confirms that the proposed method outperforms the competing methods.
Ann. Statist., Volume 47, Number 6 (2019), 3413-3437.
Received: February 2018
Revised: October 2018
First available in Project Euclid: 31 October 2019
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62C20: Minimax procedures
Secondary: 62F15: Bayesian inference 62C12: Empirical decision procedures; empirical Bayes procedures
Lee, Kyoungjae; Lee, Jaeyong; Lin, Lizhen. Minimax posterior convergence rates and model selection consistency in high-dimensional DAG models based on sparse Cholesky factors. Ann. Statist. 47 (2019), no. 6, 3413--3437. doi:10.1214/18-AOS1783. https://projecteuclid.org/euclid.aos/1572487398
- Minimax Posterior Convergence Rates and Model Selection Consistency in High-dimensional DAG Models based on Sparse Cholesky Factors. We present the proofs for the main results and other auxiliary results.