MVR: Mean-Variance Regularization
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), 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-statistics (F-statistics to follow),
(iii) Generation of diverse diagnostic plots,
(iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.