Robustifying Model Fitting
Robustifying Model Fitting
复制标题
稳健模型拟合
DOI：
10.1111/j.2517-6161.1995.tb02050.x
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发表时间：
1995
期刊：
影响因子：
--
通讯作者：
M. P. Windham
中科院分区：
文献类型：
--
作者：
M. P. Windham
从参数族拟合模型的稳健方法是有效统计分析的基础。一个程序给出了鲁棒任何模型拟合过程中使用的权重从家庭中的模型是要选择的。加权减少了与模型族不兼容的信息的影响，同时保持了熟悉的模型拟合过程的基本结构。该过程产生模型拟合函数的参数族。参数的值确定加权影响鲁棒模型拟合的程度。描述了用于确定参数的适当值的机制。
A robust method for fitting a model from a parametric family is fundamental to effective statistical analysis. A procedure is given for robustifying any model fitting process by using weights from the family from which the model is to be chosen. The weighting reduces the influence of information that is not compatible with the model family, while maintaining the basic structure of a familiar model fitting process. The procedure produces a parametric family of model fitting functions. The value of the parameter determines the degree to which the weighting influences the robustified model fit. A mechanism for determining an appropriate value for the parameter is described.