Estimation using penalized quasilikelihood and quasi-pseudo-likelihood in Poisson mixed models |
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Authors: | Xihong Lin |
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Institution: | (1) Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA |
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Abstract: | 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. |
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Keywords: | Asymptotic bias Estimating equations Generalized linear mixed models Laplace expansion Overdispersion Variance components |
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