On bias reduction in exponential and non-exponential family regression models
On bias reduction in exponential and non-exponential family regression models
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DOI：
10.1080/03610919808813491
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发表时间：
1998-01-01
影响因子：
0.9
通讯作者：
Cribari-Neto, F
中科院分区：
文献类型：
--
作者：
Cordeiro, GM;Cribari-Neto, F
本文研究了广义线性模型和非指数族非线性回归模型中极大似然估计的偏倚缩减问题。我们研究了线性预测量中参数估计量和均值估计量的偏差。我们还考虑了精度参数的估计在参数空间的不同区域的偏差。对某些特殊情况给出了简单的计算公式。通过模拟比较了最大似然估计和有偏估计的有限样本行为。我们的研究结果涵盖了一些重要的和常用的模型。
This paper addresses the issue of bias reduction of maximum likelihood estimators in generalized linear models and non-exponential family nonlinear regression models. We study both the bias of the estimators of the parameters in the linear predictors and of the means. We also consider the bias of the estimator of the precision parameter at different regions of the parameter space. Simple formulae are given for some special cases. The finite-sample behavior of maximum likelihood estimators and their bias-corrected counterparts is compared through simulation. Our results cover a number of important and commonly used models.