Weighted likelihood equations with bootstrap root search
Weighted likelihood equations with bootstrap root search
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DOI：
10.2307/2670124
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发表时间：
1998-06-01
影响因子：
3.7
通讯作者：
Lindsay, BG
中科院分区：
文献类型：
--
作者：
Markatou, M;Basu, A;Lindsay, BG
我们讨论了加权似然方程的方法，目的是获得充分有效和稳健的估计。我们讨论的情况下，连续概率模型使用单峰加权函数。这些加权函数降低了与假设模型不一致的观测值的权重。因此，在真实模型下，所提出的估计方程表现得像普通的似然方程。我们调查的估计方程的解决方案，通过自助根搜索的数量，得到的估计是一致的，渐近正态的，并具有良好的鲁棒性。广泛的模拟研究和真实的数据的例子说明了所提出的方法的操作特性。
We discuss a method of weighting likelihood equations with the aim of obtaining fully efficient and robust estimators. We discuss the case of continuous probability models using unimodal weighting functions. These weighting functions downweight observations that are inconsistent with the assumed model. At the true model, therefore, the proposed estimating equations behave like the ordinary likelihood equations. We investigate the number of solutions of the estimating equations via a bootstrap root search; the estimators obtained are consistent and asymptotically normal and have desirable robustness properties. An extensive simulation study and real data examples illustrate the operating characteristics of the proposed methodology.