The asymptotic covariance matrix of the odds ratio parameter estimator in semiparametric log-bilinear odds ratio models

The association between two random variables is often of primary interest in statistical research. In this paper semiparametric models for the association between random vectors X and Y are considered which leave the marginal distributions arbitrary. Given that the odds ratio function comprises the whole information about the association, the focus is on bilinear log-odds ratio models and in particular on the odds ratio parameter vector θ . The covariance structure of the maximum likelihood estimator θ ^ of θ is of major importance for asymptotic inference. To this end different representations of the estimated covariance matrix are derived for conditional and unconditional sampling schemes and different asymptotic approaches depending on whether X and/or Y has finite or arbitrary support. The main result is the invariance of the estimated asymptotic covariance matrix of θ ^ with respect to all above approaches. As applications we compute the asymptotic power for tests of linear hypotheses about θ —with emphasis to logistic and linear regression models—which allows to determine the necessary sample size to achieve a wanted power.