Estimating overdispersion when fitting a generalized linear model to sparse data
稀疏数据广义线性模型中的过度离散估计
We consider the problem of fitting a generalized linear model to overdispersed data, focussing on a quasilikelihood approach in which the variance is assumed to be proportional to that specified by the model, and the constant of proportionality, ϕ, is used to obtain appropriate standard errors and model comparisons. It is common practice to base an estimate of ϕ on Pearson’s lack-of-fit statistic, with or without Farrington’s modification. We propose a new estimator that has…