Posterior consistency of factor dimensionality in high-dimensional sparse factor models
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Publication:6202918
DOI10.1214/21-ba1261OpenAlexW3154792754MaRDI QIDQ6202918
Publication date: 27 February 2024
Published in: Bayesian Analysis (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/journals/bayesian-analysis/volume-17/issue-2/Posterior-Consistency-of-Factor-Dimensionality-in-High-Dimensional-Sparse-Factor/10.1214/21-BA1261.full
covariance matrixfactor modelposterior contraction rateIndian buffet processposterior consistencyfactor dimensionality
Cites Work
- Sufficient forecasting using factor models
- Empirical Bayes posterior concentration in sparse high-dimensional linear models
- High dimensional covariance matrix estimation using a factor model
- Factor modeling for high-dimensional time series: inference for the number of factors
- High-dimensional covariance matrix estimation in approximate factor models
- Identifying the number of factors from singular values of a large sample auto-covariance matrix
- Nonparametric Bayesian sparse factor models with application to gene expression modeling
- Factor models and variable selection in high-dimensional regression analysis
- Bayesian linear regression with sparse priors
- Needles and straw in a haystack: posterior concentration for possibly sparse sequences
- Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator
- Model assisted variable clustering: minimax-optimal recovery and algorithms
- Two-group classification with high-dimensional correlated data: a factor model approach
- Optimal estimation and rank detection for sparse spiked covariance matrices
- Rate-optimal posterior contraction for sparse PCA
- Large covariance estimation through elliptical factor models
- Posterior contraction in sparse Bayesian factor models for massive covariance matrices
- Eigenvalue Ratio Test for the Number of Factors
- A general framework for multiple testing dependence
- Sparse Bayesian infinite factor models
- Forecasting Using Principal Components From a Large Number of Predictors
- Estimating False Discovery Proportion Under Arbitrary Covariance Dependence
- Some estimates of norms of random matrices
- FarmTest: Factor-Adjusted Robust Multiple Testing With Approximate False Discovery Control
- Expandable factor analysis
- High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics
- Determining the Number of Factors in Approximate Factor Models
- Large Covariance Estimation by Thresholding Principal Orthogonal Complements