Testing for complete independence in high dimensions

By James R. Schott. 2005

175 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c79a255c7a667fa50fe3834163713392bd6347d0

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In Statistics and Probability

Citations in the corpus

The distance correlation t-test of independence in high dimension (2013, background): “Recent papers that address the problem of testing independence in high dimension include Schott [15] who considers multivariate normal data, Ledoit and Wolf [9] concerning tests for the covariance matrix, and Heer [6].”
THE ASYMPTOTIC DISTRIBUTION AND BERRY-ESSEEN BOUND OF A NEW TEST FOR INDEPENDENCE IN HIGH DIMENSION WITH AN APPLICATION TO STOCHASTIC OPTIMIZATION (2008, methodology): “1 the complete independence of the p components of X. Johnstone [15] uses the largest eigenvalue of the sample covariance matrix and Ledoit and Wolf [17] use quadratic forms of the sample covariance matrix eigenvalues to test the null hypothesis H0 :Σ = Ip, where Ip is the p× p identity matrix, whil”
Testing Cross-Sectional Correlation in Large Panel Data Models with Serial Correlation (2016, background): “For example, Pesaran et al. [11] consider an LM test and correct its bias in a large heterogeneous panel data model; Baltagi et al. [12] extend Schott’s test [9] to a fixed effects panel data model and correct the bias caused by estimating the disturbances with fixed effects residuals in a homogeneo”
Multivariate sign-based high-dimensional tests for sphericity (2014, methodology): “Some new methods were proposed by Chen & Qin (2010) for a two-sample test for means, Ledoit & Wolf (2002), Schott (2005) and Chen et al. (2010) for testing a specific covariance 20 structure, and Tang & Leng (2010) and the references therein for variable selection.”
High-dimensional consistent independence testing with maxima of rank correlations (2018, methodology): “There, the authors combined Nagao’s L2-type methods with rank and distance covariance statistics that in a tour de force are shown to weakly converge to a Gaussian limit under the null.”
Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation (2012, methodology): “Tibshirani et al. (2005) analyzed the data by the fused Lasso.”
A two-sample test for high-dimensional data with applications to gene-set testing (2010, background): “Testing certain special structures of the covariance matrix when p and n are of the same order have been considered in Ledoit and Wolf (2002) and Schott (2005). We assume”
New Weighted Portmanteau Statistics for Time Series Goodness of Fit Testing (2012, unknown): “Consider the problem discussed in Srivastava (2005) for testing for an identity covariance matrix.”
Testing independence in high dimensions with sums of rank correlations (2015, methodology): “Our proof of Theorem 4.1 is based on a central limit theorem for martingale arrays Hall and Heyde (1980), Corollary 3.1, that was also applied by Schott (2005).”
High dimensional correlation matrices: the central limit theorem and its applications (2017, methodology): “Schott (2005) utilized sample correlation matrices to test independence for a large number of random variables having a multivariate normal distribution.”
Testing mutual independence in high dimensions with sums of squares of rank correlations (2015, methodology): “The theoretical de- velopments parallel that of Schott (2005) who considered Pearson correlations in the context of normal observations.”
Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem (2016, background): “…Dempster (1958), Bai and Saranadasa (1996), Srivastava andDu (2008), andChen andQin (2010) for two-sample tests for means, Ledoit and Wolf (2002), Schott (2005), Chen, Zhang, and Zhong (2010), and Zou et al. (2014) for testing a specific covariance structure, Goeman, Van CONTACT Zhaojun Wang…”
Likelihood Ratio Tests for High‐Dimensional Normal Distributions (2013, methodology): “For example, in the field of multivariate analysis, Schott (2001, 2005, 2007), Ledoit & Wolf (2002), Bai et al. (2009), Chen et al. (2010), Jiang et al. (2012), and Jiang & Yang (2013) study the classical Likelihood Ratio Tests (LRTs) when p is large.”