论文标题
高斯转换建模和分布回归函数的估计
Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions
论文作者
论文摘要
我们提出了有条件累积分布函数的灵活高斯表示，并给出了其估计的坚固可能性标准。最佳表示满足条件累积分布函数的单调性能，包括在有限样本中和一般错误指定。我们使用这些表示形式为有条件密度，累积分布和分位数功能以参数速率的柔性最大似然估计提供统一的框架。我们的公式对相关方法进行了实质性的简化和有限的样本改进。在美国的性别工资差距上的经验应用说明了我们的框架。
We propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and under general misspecification. We use these representations to provide a unified framework for the flexible Maximum Likelihood estimation of conditional density, cumulative distribution, and quantile functions at parametric rate. Our formulation yields substantial simplifications and finite sample improvements over related methods. An empirical application to the gender wage gap in the United States illustrates our framework.
