Comparing variances of correlated variables Export

@article{Cohen1985, abstract = {Abstract\ \ A test is proposed for the equality of the variances ofk 2 correlated variables. Pitman's test fork = 2 reduces the null hypothesis to zero correlation between their sum and their difference. Its extension, eliminating nuisance parameters by a bootstrap procedure, is valid for any correlation structure between thek normally distributed variables. A Monte Carlo study for several combinations of sample sizes and number of variables is presented, comparing the level and power of the new method with previously published tests. Some nonnormal data are included, for which the empirical level tends to be slightly higher than the nominal one. The results show that our method is close in power to the asymptotic tests which are extremely sensitive to nonnormality, yet it is robust and much more powerful than other robust tests.}, author = {Cohen, Ayala}, citeulike-article-id = {5860340}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/BF02294062}, citeulike-linkout-1 = {http://www.springerlink.com/content/554737627q631647}, day = {27}, doi = {10.1007/BF02294062}, journal = {Psychometrika}, keywords = {bootstrap, variance}, month = {September}, number = {3}, pages = {379--391}, posted-at = {2009-09-30 17:18:14}, priority = {2}, title = {Comparing variances of correlated variables}, url = {http://dx.doi.org/10.1007/BF02294062}, volume = {51}, year = {1986} }

TY - JOUR ID - Cohen1985 L3 - citeulike-article-id:5860340 N2 - Abstract  A test is proposed for the equality of the variances ofk 2 correlated variables. Pitman's test fork = 2 reduces the null hypothesis to zero correlation between their sum and their difference. Its extension, eliminating nuisance parameters by a bootstrap procedure, is valid for any correlation structure between thek normally distributed variables. A Monte Carlo study for several combinations of sample sizes and number of variables is presented, comparing the level and power of the new method with previously published tests. Some nonnormal data are included, for which the empirical level tends to be slightly higher than the nominal one. The results show that our method is close in power to the asymptotic tests which are extremely sensitive to nonnormality, yet it is robust and much more powerful than other robust tests. IS - 3 JF - Psychometrika EP - 391 TI - Comparing variances of correlated variables VL - 51 SP - 379 KW - bootstrap KW - variance AU - Cohen, Ayala PY - 1986/09/27/ UR - http://dx.doi.org/10.1007/BF02294062 ER -