Posterior contraction in group sparse logit models for categorical responses

Posterior Contraction in Sparse Bayesian Factor Models for Massive Covariance

Sparse Bayesian factor models are routinely implemented for parsimonious dependence modeling and dimensionality reduction in highdimensional applications. We provide theoretical understanding of such Bayesian procedures in terms of posterior convergence rates in inferring high-dimensional covariance matrices where the dimension can be potentially larger than the sample size. Under relevant spar...

Rate-optimal Posterior Contraction for Sparse Pca

Principal component analysis (PCA) is possibly one of the most widely used statistical tools to recover a low rank structure of the data. In the high-dimensional settings, the leading eigenvector of the sample covariance can be nearly orthogonal to the true eigenvector. A sparse structure is then commonly assumed along with a low rank structure. Recently, minimax estimation rates of sparse PCA ...

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Rate-optimal Posterior Contraction for Sparse Pca By

Principal component analysis (PCA) is possibly one of the most widely used statistical tools to recover a low-rank structure of the data. In the highdimensional settings, the leading eigenvector of the sample covariance can be nearly orthogonal to the true eigenvector. A sparse structure is then commonly assumed along with a low rank structure. Recently, minimax estimation rates of sparse PCA w...

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