ABSTRACT
In this paper we derive explicit expressions for the elements of the exact Fisher information matrix of the Dirichlet-multinomial distribution. We show that exact calculation is based on the beta-binomial probability function rather than that of the Dirichlet-multinomial and this makes the exact calculation quite easy. The exact results are expected to be useful for the calculation of standard errors of the maximum likelihood estimates of the beta-binomial parameters and those of the Dirichlet-multinomial parameters for data that arise in practice in toxicology and other similar fields. Standard errors of the maximum likelihood estimates of the beta-binomial parameters and those of the Dirichlet-multinomial parameters, based on the exact and the asymptotic Fisher information matrix based on the Dirichlet distribution, are obtained for a set of data from Haseman and Soares (1976), a dataset from Mosimann (1962) and a more recent dataset from Chen, Kodell, Howe and Gaylor (1991). There is substantial difference between the standard errors of the estimates based on the exact Fisher information matrix and those based on the asymptotic Fisher information matrix.
Subject(s)
Statistical Distributions , Abnormalities, Drug-Induced , Animals , Biometry , Data Interpretation, Statistical , Female , Hydroxyurea/toxicity , Likelihood Functions , Male , Mice , Models, Statistical , Multivariate Analysis , Mutagenicity Tests/statistics & numerical data , Pollen , PregnancyABSTRACT
A procedure for testing for treatment effect in data similar to the data on premature ventricular contractions (PVC) is presented. We consider a zero-inflated beta-binomial model. Based on this model, we develop score tests to test for treatment effect in the data in which observations in the form of counts are recorded before and after applying a therapy. Results of a small simulation experiment, to study small sample behaviour of a score test and a likelihood ratio test, are reported and the PVC data are analysed. Both the score and the likelihood ratio tests show good level properties. Either the score tests or the likelihood ratio tests can be used for testing the presence of treatment effect. The score tests, however, may be preferable because they use estimates of the parameters only under the null hypothesis and in the important range pi<0.5 power of the score test statistic S1 is slightly better than the likelihood ratio statistic LR1.
