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Estimation using penalized quasilikelihood and quasi-pseudo-likelihood in Poisson mixed models

机译:Poisson混合模型中使用罚拟似然和拟伪似然的估计

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We consider two estimation schemes based on penalized quasilikelihood and quasi-pseudo-likelihood in Poisson mixed models. The asymptotic bias in regression coefficients and variance components estimated by penalized quasilikelihood (PQL) is studied for small values of the variance components. We show the PQL estimators of both regression coefficients and variance components in Poisson mixed models have a smaller order of bias compared to those for binomial data. Unbiased estimating equations based on quasi-pseudo-likelihood are proposed and are shown to yield consistent estimators under some regularity conditions. The finite sample performance of these two methods is compared through a simulation study.
机译:我们考虑在Poisson混合模型中基于惩罚拟似然和拟伪似然的两种估计方案。对于方差分量的小值,研究了通过惩罚拟似然(PQL)估计的回归系数和方差分量的渐近偏差。我们显示,与二项式数据相比,泊松混合模型中回归系数和方差分量的PQL估计量具有较小的偏差阶数。提出了基于拟伪似然的无偏估计方程,并证明了在某些规则性条件下产生一致的估计。通过仿真研究比较了这两种方法的有限样本性能。

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