Bias Correction in Generalized Linear Mixed Models With Multiple Components of Dispersion
推导了广义线性混合模型中惩罚拟似然估计的渐近偏差通用公式，提出一阶和二阶校正方法以减少回归系数和方差分量的偏差，并通过蝾螈交配实验数据与模拟研究验证效果。
Abstract General formulas are derived for the asymptotic bias in regression coefficients and variance components estimated by penalized quasi-likelihood (PQL) in generalized linear mixed models with canonical link function and multiple sets of independent random effects. Easily computed correction matrices result in variance component estimates that have satisfactory asymptotic behavior for small values of the variance components and significantly reduce bias for larger values. Both first-order and second-order correction procedures are developed for regression coefficients estimated by PQL. The methods are illustrated through an analysis of an experiment on salamander matings involving crossed male and female random effects, and their properties are evaluated in a simulation study.