Power of edge exclusion tests in graphical gaussian models

Asymptotic multivariate normal approximations to the joint distributions of edge exclusion test statistics for saturated graphical Gaussian models are derived. Non-signed and signed square-root versions of the likelihood ratio, Wald and score test statistics are considered. Non-central chi-squared approximations are also considered for the non-signed versions. These approximations are used to estimate the power of edge exclusion tests and an example is presented.<br/

Power of edge exclusion tests for graphical log-linear models

Asymptotic multivariate normal approximations to the joint distributions of edge exclusion test statistics for saturated graphical log-linear models, with all variables binary, are derived. Non-signed and signed square-root versions of the likelihood ratio, Wald and score test statistics are considered. Non-central chi-squared approximations are also considered for the non-signed versions of the test statistics. Simulation results are used to assess the quality of the proposed approximations. These approximations are used to estimate the overall power of edge exclusion tests. Power calculations are illustrated using data onuniversity admissions