A generic algorithm for reducing bias in parametric estimation
A generic algorithm for reducing bias in parametric estimation
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DOI：
10.1214/10-ejs579
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发表时间：
2010-01-01
影响因子：
1.1
通讯作者：
Firth, David
中科院分区：
文献类型：
--
作者：
Kosmidis, Ioannis;Firth, David
开发了一种通用迭代算法，用于通过调整得分函数来计算常规统计模型中的减少偏差参数估计。该算法统一了之前针对某些特定模型类发布的迭代方法，并为其提供了有吸引力的新解释。新算法可以完全被视为一系列迭代偏差校正，从而有助于在已经导出最大似然估计器的一阶偏差的任何模型中采用调整评分方法来减少偏差。该方法通过应用于具有 beta 分布响应的 logit 线性多元回归模型进行了测试；结果证实了新算法的有效性，同时也揭示了现有 beta 回归文献中的一些重要错误。
A general iterative algorithm is developed for the computation of reduced-bias parameter estimates in regular statistical models through adjustments to the score function. The algorithm unifies and provides appealing new interpretation for iterative methods that have been published previously for some specific model classes. The new algorithm can use fully be viewed as a series of iterative bias corrections, thus facilitating the adjusted score approach to bias reduction in any model for which the first order bias of the maximum likelihood estimator has already been derived. The method is tested by application to a logit-linear multiple regression model with beta-distributed responses; the results confirm the effectiveness of the new algorithm, and also reveal some important errors in the existing literature on beta regression.