Two‐sample test of high dimensional means under dependence

By Tommaso Cai, Weidong Liu, Yin Xia. 2014

322 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

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Citations in the corpus

Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing (2015, background): “Another related line of work analyzes the setting where p could be exponentially larger than n but assuming some kind of sparsity (say in the mean difference); see Cai et al. [2014] for such an example.”
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives (2015, background): “Another direction involves the study of sparse alternatives, where δ is sparse, as done by Cai et al. (2014).”
TWO-SAMPLE BEHRENS-FISHER PROBLEM FOR HIGH-DIMENSIONAL DATA (2015, background): “In view of this, the standardized version with shrinkage estimation under sparse structure and other conditions may help; see Cai et al. (2014).”
Two-sample tests for high-dimension, strongly spiked eigenvalue models (2016, background): “When π 1 and π 2 are non-Gaussian, Bai and Saranadasa (1996) and Cai, Liu, and Xia (2014) considered the test under homoscedasticity, Σ 1 = Σ 2 .”
Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem (2016, methodology): “In a significant development in another direction that using themax-norm rather than theL2-norm,Cai, Liu, and Xia (2014) proposed a test based on the max-norm of marginal t-statistics.”
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives (2014, background): “Another direction involves the study of sparse alternatives, where δ is sparse, as done by Cai et al. (2014).”
High-dimensional change-point detection with sparse alternatives (2013, background): “Note that Problem (P1) corresponds to a two-sample test problem with mean-difference in a subset of components and same variance; see (Cai, Liu and Xia, 2014) for a review of recent works on this topic. imsart-aos ver.”
A Simple Two-Sample Test in High Dimensions Based on L2-Norm (2020, methodology): “In this simulation study, we compare L2N and L2D with the tests proposed by Srivastava and Kubokawa (2013), Cai, Liu, and Xia (2014) and Gregory et al. (2015), denoted as SK, CLX, and GCBL, respectively.”
Simultaneous Inference for High-Dimensional Linear Models (2016, result): “This is in sharp contrast with the extreme value approach in Cai et al. (2014); see Remark 2.4.”