The iteratively reweighted estimating equation in minimum distance problems
The iteratively reweighted estimating equation in minimum distance problems
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DOI：
10.1016/s0167-9473(02)00326-2
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发表时间：
2004-03-01
影响因子：
1.8
通讯作者：
Lindsay, BG
中科院分区：
文献类型：
--
作者：
Basu, A;Lindsay, BG
基于密度的最小距离估计器提供了极大似然估计的另一种有吸引力的选择，因为这类估计器的几个成员具有良好的稳健性，同时在假设的模型下是一阶有效的。引入了一种有用的计算技术--类似于稳健回归中使用的迭代加权最小二乘法--使这些估计器在计算上更加可行。该方法的实现比牛顿-拉夫森(NR)方法简单得多。在某些指数族情况下，只需对权函数稍加修改，就可以消除与NR方法相比在收敛速度上的损失--在这种情况下，其性能与NR方法相当。对于大量的参数，这种改进版本的性能实际上预计会比NR方法更好。鉴于人们对基于密度的稳健性方法的广泛兴趣，这种修改似乎具有很大的实用价值。(C)2002 Elsevier B.V.保留所有权利。
The class of density based minimum distance estimators provide attractive alternatives to the maximum likelihood estimator because several members of this class have nice robustness properties while being first-order efficient under the assumed model. A helpful computational technique-similar to the iteratively reweighted least squares used in robust regression-is introduced which makes these estimators computationally much more feasible. This technique is much simpler than the Newton-Raphson (NR) method to implement. The loss suffered in the rate of convergence compared to the NR method can be made to vanish in some exponential family situations by a little modification in the weight function-in which case the performance is comparable to the NR method. For a large number of parameters the performance of this modified version is actually expected to be better than the NR method. In view of the widespread interest in density based robust procedures, this modification appears to be of great practical value. (C) 2002 Elsevier B.V. All rights reserved.