Pairwise-Difference Rank Estimation of the Transformation Model
提出变换模型中系数向量的对偶差分秩估计量，无需主观选择带宽，通过蒙特卡洛模拟和实证应用表明其性能优于现有半参数估计量。
This article considers pairwise-difference rank estimators of the coefficient vector in a transformation model. These estimators, like other existing rank estimators, require no subjective bandwidth choice. Monte Carlo simulations, numerical asymptotic efficiency comparisons, and two empirical applications suggest that the proposed estimators perform well in comparison with existing semiparametric estimators.