Two sample test for high-dimensional partially paired data
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Publication:5130309
DOI10.1080/02664763.2015.1014890OpenAlexW2094402359MaRDI QIDQ5130309
Cheol-Keun Park, Johan Lim, Insuk Sohn, Sin-Ho Jung, Seokho Lee
Publication date: 4 November 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02664763.2015.1014890
high-dimensional datapartially paired datamicroarray experimentregularized Hotelling's \(t\)-statisticshrinkage covariance matrix estimatortesting the equality ofmean vectors
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Cites Work
- Unnamed Item
- A well-conditioned estimator for large-dimensional covariance matrices
- Permutation test for incomplete paired data with application to cDNA microarray data
- Order test for high-dimensional two-sample means
- Detection of differentially expressed gene sets in a partially paired microarray data set
- Eigenvectors of some large sample covariance matrix ensembles
- A two-sample test for high-dimensional data with applications to gene-set testing
- A test for the mean vector with fewer observations than the dimension
- Asymptotic properties of a two sample randomized test for partially dependent data
- A Regularized Hotelling’sT2Test for Pathway Analysis in Proteomic Studies
- Maximum Likelihood Estimates for a Multivariate Normal Distribution when some Observations are Missing
- Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices
- On comparing means in the paired case with incomplete data on both responses
- Testing equality of means of correlated variates with missing observations on both responses
- On difference of means with incomplete data
- Notes on two sample tests for partially correlated (paired) data
- DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES
- Condition-Number-Regularized Covariance Estimation
