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A low-rank based estimation-testing procedure for matrix-covariate regression
[article]
2016
arXiv
pre-print
Matrix-covariate is now frequently encountered in many biomedical researches. It is common to fit conventional statistical models by vectorizing matrix-covariate. This strategy, however, results in a large number of parameters, while the available sample size is relatively too small to have reliable analysis results. To overcome the problem of high-dimensionality in hypothesis testing, variance component test has been proposed with promise detection power, but is not straightforward to provide
arXiv:1607.02957v1
fatcat:4cqtutnqzjadrdq5uyad4zv4za