NONPARAMETRIC ANALYSIS OF A GENERALIZED REGRESSION-MODEL - THE MAXIMUM RANK CORRELATION ESTIMATOR
NONPARAMETRIC ANALYSIS OF A GENERALIZED REGRESSION-MODEL - THE MAXIMUM RANK CORRELATION ESTIMATOR
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DOI：
10.1016/0304-4076(87)90030-3
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发表时间：
1987-07-01
影响因子：
6.3
通讯作者：
HAN, AK
中科院分区：
文献类型：
--
作者：
HAN, AK
本文考虑了一类模型yi=D·F(x‘iβ0，ui)的估计问题，其中复合变换D·F仅指定D：R→R是非退化单调的，F：R2→R是严格单调的.因此，本文推广了标准数据分析，它假定D·F的函数形式是已知的和可加的。所提出的估计量是最大秩相关估计量，它是D·F的函数形式的非参数且误差项的分布是非参数的，并证明了该估计量对参数β0直到尺度系数都是强相合的。
The paper considers estimation of a model y i= D· F (x′ i β 0, u i), where the composite transformation D· F is only specified that D: R→ R is non-degenerate monotonic and F: R 2→ R is strictly monotonic in each of its variables. The paper thus generalizes standard data analysis which assumes that the functional form of D· F is known and additive. The estimator which it proposes is the maximum rank correlation estimator which is non-parametric in the functional form of D· F and non-parametric in the distribution of the error terms, u i. The estimator is shown to be strongly consistent for the parameters β 0 up to a scale coefficient.