Mean-Variance Regularization
Description
MVR is a non-parametric method for joint adaptive
mean-variance regularization and variance stabilization of
high-dimensional data. It is suited for handling difficult
problems posed by high-dimensional multivariate datasets (p >>
n paradigm), such as in omics-type data, among which are that
the variance is often a function of the mean, variable-specific
estimators of variances are not reliable, and tests statistics
have low powers due to a lack of degrees of freedom. Key
features include (i) Normalization and/or variance
stabilization of the data, (ii) Computation of
mean-variance-regularized t- and F-statistics, (iii) Generation
of diverse diagnostic plots, (iv) Computationally efficiency
implementation, using C++ interfacing, and an option for
parallel computing to enjoy a fast and easy experience in the R
environment.