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Sparse Principal Component Analysis and Iterative Thresholding

- Zongming Ma
- Mathematics
- 12 December 2011

Principal component analysis (PCA) is a classical dimension reduction method which projects data onto the principal subspace spanned by the leading eigenvectors of the covariance matrix. However, it… Expand

Optimal Rates of Convergence for Noisy Sparse Phase Retrieval via Thresholded Wirtinger Flow

- T. Cai, X. Li, Zongming Ma
- Mathematics, Computer Science
- ArXiv
- 10 June 2015

TLDR

Sparse PCA: Optimal rates and adaptive estimation

- T. Cai, Zongming Ma, Y. Wu
- Mathematics
- 6 November 2012

Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications. This paper considers both minimax and adaptive estimation of the… Expand

Sparse CCA: Adaptive Estimation and Computational Barriers

- C. Gao, Zongming Ma, Harrison H. Zhou
- Mathematics
- 30 September 2014

Canonical correlation analysis is a classical technique for exploring the relationship between two sets of variables. It has important applications in analyzing high dimensional datasets originated… Expand

Achieving Optimal Misclassification Proportion in Stochastic Block Models

- C. Gao, Zongming Ma, A. Zhang, Harrison H. Zhou
- Computer Science, Mathematics
- J. Mach. Learn. Res.
- 14 May 2015

TLDR

Nonparametric methods for doubly robust estimation of continuous treatment effects.

- Edward H. Kennedy, Zongming Ma, M. McHugh, Dylan S. Small
- Mathematics, Medicine
- Journal of the Royal Statistical Society. Series…
- 2 July 2015

Continuous treatments (e.g., doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing… Expand

Optimal hypothesis testing for high dimensional covariance matrices

- T. Cai, Zongming Ma
- Mathematics
- 18 May 2012

This paper considers testing a covariance matrixin the high dimensional setting where the dimension p can be comparable or much larger than the sample size n. The problem of testing the hypothesis H0… Expand

Optimal estimation and rank detection for sparse spiked covariance matrices

- T. Cai, Zongming Ma, Y. Wu
- Mathematics, Medicine
- Probability theory and related fields
- 14 May 2013

This paper considers a sparse spiked covariance matrix model in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the… Expand

Accuracy of the Tracy–Widom limits for the extreme eigenvalues in white Wishart matrices

- Zongming Ma
- Mathematics
- 1 February 2012

The distributions of the largest and the smallest eigenvalues of a $p$-variate sample covariance matrix $S$ are of great importance in statistics. Focusing on the null case where $nS$ follows the… Expand

Computational Barriers in Minimax Submatrix Detection

- Zongming Ma, Y. Wu
- Mathematics, Computer Science
- ArXiv
- 23 September 2013

TLDR

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