Variance stabilizing filters#

Optimization of variance-stabilizing transformations

Variance-stabilizing transformations are commonly exploited in order to make non-homoskedastic data easily tractable by standard methods. However, for the most common families of distributions (e.g., binomial, Poisson, etc.) exact stabilization is not possible and even achieving some approximate stabilization turns out to be rather challenging. We approach the variance stabilization problem as ...

Variance-stabilizing transformations for two-color microarrays

MOTIVATION Authors of several recent papers have independently introduced a family of transformations (the generalized-log family), which stabilizes the variance of microarray data up to the first order. However, for data from two-color arrays, tests for differential expression may require that the variance of the difference of transformed observations be constant, rather than that of the trans...

Variance Stabilizing Power Transformation for Time Series

Variance-stabilizing and Confidence-stabilizing Transformations for the Normal Correlation Coefficient with Known Variances

Fosdick and Raftery (2012) revisited the classical problem of inference for a bivariate normal correlation coefficient ρ when the variances are known. They considered several frequentist and Bayesian estimators, the former including the maximum likelihood estimator (MLE), but did not obtain the standard errors of these estimators or confidence intervals for ρ. Here we present a new variance-sta...

Classroom Simulation: Are Variance-stabilizing Transformations Really Useful

When population variances of observations in an ANOVA are a known function of their population means, many textbooks recommend using variancestabilizing transformations. Examples are: square root transformation for Poisson data, arcsine of square root for binomial proportions, and log for exponential data. We investigate the usefulness of transformations in onefactor, 3-level ANOVAs with nonnor...