ArticleJournal of multivariate analysis2016
Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data.
Article in Journal of multivariate analysis, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- High-dimensional principal component analysis with heterogeneous missingness.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
- Correlation Imputation for Single-Cell RNA-seq.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Minimax Rates ofEntropy (Basel, Switzerland) · 2021Article
- Optimal Sparse Linear Prediction for Block-missing Multi-modality Data without Imputation.Journal of the American Statistical Association · 2020Article
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2 authors.
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Abstract
Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the sense that the missingness is not dependent on the values of the data. Based on incomplete data, estimators for bandable and sparse covariance matrices are proposed and their theoretical and numerical properties are investigated. Minimax rates of convergence are established under the spectral norm loss and the proposed estimators are shown to be rate-optimal under mild regularity conditions. Simulation studies demonstrate that the estimators perform well numerically. The methods are also illustrated through an application to data from four ovarian cancer studies. The key technical tools developed in this paper are of independent interest and potentially useful for a range of related problems in high-dimensional statistical inference with missing data.
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