title: Influence diagnostics for count data under AB-BA crossover trials.
abstract: This paper aims to develop diagnostic measures to assess the influence of data perturbations on estimates in AB-BA crossover studies with a Poisson distributed response. Generalised mixed linear models with normally distributed random effects are utilised. We show that in this special case, the model can be decomposed into two independent sub-models which allow to derive closed-form expressions to evaluate the changes in the maximum likelihood estimates under several perturbation schemes. The performance of the new influence measures is illustrated by simulation studies and the analysis of a real dataset.
authors.affiliation: 1 Department of Statistics, Stockholm University, Stockholm, Sweden.
authors.forename: Chengcheng
authors.initials: C
authors.lastname: Hao
authors.affiliation: 2 Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden.|3 Department of Mathematics, Linköping University, Linköping, Sweden.
authors.forename: Dietrich
authors.initials: D
authors.lastname: von Rosen
authors.affiliation: 1 Department of Statistics, Stockholm University, Stockholm, Sweden.
authors.forename: Tatjana
authors.initials: T
authors.lastname: von Rosen
mesh_terms: D000787:Angina Pectoris / Q000188:drug therapy; D056808:Biostatistics / Q000379:methods; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D018592:Cross-Over Studies; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D016012:Poisson Distribution
keywords: Generalised mixed linear model; Influential observation; Poisson model; model diagnostics; perturbation scheme