Local influence diagnostics for incomplete overdispersed longitudinal counts
Local influence diagnostics for incomplete overdispersed longitudinal counts
We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is …