Premium
Advantages of Variance Stabilization
Author(s) -
MORGENTHALER STEPHAN,
STAUDTE ROBERT G.
Publication year - 2012
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2011.00768.x
Subject(s) - mathematics , confidence interval , variance (accounting) , divergence (linguistics) , statistics , sample size determination , statistic , context (archaeology) , statistical hypothesis testing , simple (philosophy) , delta method , null hypothesis , multiple comparisons problem , statistical power , measure (data warehouse) , econometrics , computer science , data mining , paleontology , linguistics , philosophy , accounting , epistemology , estimator , business , biology
. Variance stabilization is a simple device for normalizing a statistic. Even though its large sample properties are similar to those of studentizing , many simulation studies of confidence interval procedures show that variance stabilization works better for small samples. We investigated this question in the context of testing a null hypothesis involving a single parameter. We provide support for a measure of evidence for an alternative hypothesis that is simple to compute, calibrate and interpret. It has applications in most routine problems in statistics, and leads to more accurate confidence intervals, estimated power and hence sample size calculations than standard asymptotic methods. Such evidence is readily combined when obtained from different studies. Connections to other approaches to statistical evidence are described, with a notable link to Kullback–Leibler symmetrized divergence.