Testing predictor contributions in sufficient dimension reduction
Testing predictor contributions in sufficient dimension reduction
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DOI：
10.1214/009053604000000292
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发表时间：
2004-06-01
影响因子：
4.5
通讯作者：
Cook, RD
中科院分区：
文献类型：
--
作者：
Cook, RD
我们对回归中选定的预测变量没有影响的假设进行测试，而不假设给定预测变量的响应条件分布模型。预测器效果不必限于均值函数，并且不需要平滑。一般方法基于充分的降维，其想法是用较低维度的版本替换预测向量，而不丢失回归信息。详细开发了使用切片逆回归的方法。
We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the conditional distribution of the response given the predictors. Predictor effects need not be limited to the mean function and smoothing is not required. The general approach is based on sufficient dimension reduction, the idea being to replace the predictor vector with a lower-dimensional version without loss of information on the regression. Methodology using sliced inverse regression is developed in detail.