title: A Monte Carlo investigation of the robustness of the Wald, likelihood Ratio, and Median tests with specified symmetric and asymmetric marginal distributions.
abstract: The effect of nonnormality on the Type I (tau) error when comparing two independent binomial proportions (P) or the nonparametric alternatives, the Median (Me), Wald (W), and Likelihood Ratio (LR), has not been investigated. If these selected tests are overly conservative the implied loss of power would moderate their practical use. The purpose of the present study was to investigate the impact of nonnormality on small to moderate sample sizes on the estimated tau for alpha = 0.10, 0.05, and 0.01 for the P, Me, W, and LR tests. Samples were generated from nine long-tailed symmetric and asymmetric distributions using a multiplicative congruential generator. For each marginal distribution and for a variety of sample sizes, the proportion of samples for which the test statistic exceeded the 10, 5, and 1 percentage points was tabulated. For data that mimic a symmetric distribution, the median test uniformly yields an empirical alpha considerably less than tau, while the likelihood ratio test consistently overestimates tau for small samples (n < or = 15) over all symmetric distributions and empirical alpha levels. For asymmetric distributions, the median test again yields an empirical alpha significantly less than tau. Similar underestimates of tau were found for the chi-square (2 df), chi-square (4 df) log normal, and gamma (2, 1) distributions. The likelihood ratio test consistently overestimates tau for small samples (n < or = 15) over all asymmetric distributions and empirical alpha levels. The independent proportions test produces an empirical alpha closest to tau for n = 10 for all asymmetric distributions.(ABSTRACT TRUNCATED AT 250 WORDS)
authors.affiliation: Research Department, Allentown Hospital--Lehigh Valley Hospital Center, PA 18105.
authors.forename: J F
authors.initials: JF
authors.lastname: Reed
mesh_terms: D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D009010:Monte Carlo Method; D016011:Normal Distribution