Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings

By Tommaso Cai, Weidong Liu, Yin Xia. 2013

262 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/27ffc974eb0553e25c4bd09f9a30f5bb92adb8cd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhao Ren
2. Fang Han
3. Mathias Drton
4. Hongjian Shi
5. Cun-Hui Zhang
6. Hang Deng
7. Tommaso Cai
8. Harrison H. Zhou

In Molecular Biology

1. Cun-Hui Zhang
2. Hang Deng
3. Hongjian Shi
4. Mathias Drton
5. Fang Han
6. Harrison H. Zhou
7. Tommaso Cai
8. Fan Yang

Citations in the corpus

• Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation (2016, background): “…test in Ledoit and Wolf (2002), Srivastava (2005), Birke and Dette (2005), and Chen et al. (2010), and maximum entrywise deviation test in Jiang (2004), Zhou (2007), Liu et al. (2008), Li et al. (2010), Li et al. (2012), Cai and Jiang (2011), Shao and Zhou (2014), and Cai, Liu, and Xia (2013).”
• Likelihood Ratio Tests for High‐Dimensional Normal Distributions (2013, background): “(iii) Recently, some authors study similar problems under the nonparametric setting, for example, the works of Cai et al. (2013), Cai & Ma (2013), Chen et al. (2010), Li & Chen (2012), Qiu & Chen (2012) and Xiao & Wu (2013).”
• High-dimensional consistent independence testing with maxima of rank correlations (2018, methodology): “We remark that, regarding the power, for L2type and maximum-type tests, one cannot dominate the other; compare the power analyses in Section 3.3 in Cai et al. (2013) and Section 5.2 in Leung and Drton (2018).”
• Beyond Gaussian approximation: Bootstrap for maxima of sums of independent random vectors (2017, methodology): “Examples include sure screening (Fan and Lv, 2008), removing spurious correlation (Fan and Zhou, 2016), testing the equality of two matrices (Cai et al., 2013; Chang et al., 2017), detecting ridges and estimating level sets (Chen et al., 2015, 2016), and many more.”
• Global Testing and Large-Scale Multiple Testing for High-Dimensional Covariance Structures (2017, methodology): “…(2009), Ravikumar et al. (2011), and Chandrasekaran et al. (2012), regularizing principal components in Johnstone and Lu (2009), Zou et al. (2006), Cai et al. (2013, 2015), Fan et al. (2013), and Vu and Lei (2013), and penalized regression for precision matrix estimation, Gaussian graphical…”