Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Random subspace method for multivariate feature selection
Pattern Recognition Letters
Computational Statistics & Data Analysis
Analyzing gene expression data in terms of gene sets
Bioinformatics
A test for the mean vector with fewer observations than the dimension
Journal of Multivariate Analysis
Using randomized projection techniques to aid in detecting high-dimensional malicious applications
Proceedings of the 49th Annual Southeast Regional Conference
Resistant estimates for high dimensional and functional data based on random projections
Computational Statistics & Data Analysis
A two sample test in high dimensional data
Journal of Multivariate Analysis
Using random subspace method for prediction and variable importance assessment in linear regression
Computational Statistics & Data Analysis
RcppArmadillo: Accelerating R with high-performance C++ linear algebra
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
A common problem in genetics is that of testing whether a set of highly dependent gene expressions differ between two populations, typically in a high-dimensional setting where the data dimension is larger than the sample size. Most high-dimensional tests for the equality of two mean vectors rely on naive diagonal or trace estimators of the covariance matrix, ignoring dependences between variables. A test using random subspaces is proposed, which offers higher power when the variables are dependent and is invariant under linear transformations of the marginal distributions. The p-values for the test are obtained using permutations. The test does not rely on assumptions about normality or the structure of the covariance matrix. It is shown by simulation that the new test has higher power than competing tests in realistic settings motivated by microarray gene expression data. Computational aspects of high-dimensional permutation tests are also discussed and an efficient R implementation of the proposed test is provided.