论文标题
对称广义heckman模型
Symmetric generalized Heckman models
论文作者
论文摘要
当感兴趣的变量与潜在变量相关时，就会出现样本选择偏见问题，并且涉及响应变量具有其观察结果的一部分情况的情况。 Heckman（1976）提出了一个基于双变量正态分布的样本选择模型，该模型既适合感兴趣的变量又适合潜在变量。最近，这种对正态性的假设已被更灵活的模型（例如Student-T分布）放松（Marchenko和Genton，2012； Lachos等，2021）。这项工作的目的是基于对称分布提出广义的Heckman样本选择模型（Fang等，1990）。这是一类新的样本选择模型，其中变量被添加到分散和相关参数中。进行了一项蒙特卡洛模拟研究，以评估参数估计方法的行为。分析了两个实际数据集，以说明所提出的方法。
The sample selection bias problem arises when a variable of interest is correlated with a latent variable, and involves situations in which the response variable had part of its observations censored. Heckman (1976) proposed a sample selection model based on the bivariate normal distribution that fits both the variable of interest and the latent variable. Recently, this assumption of normality has been relaxed by more flexible models such as the Student-t distribution (Marchenko and Genton, 2012; Lachos et al., 2021). The aim of this work is to propose generalized Heckman sample selection models based on symmetric distributions (Fang et al., 1990). This is a new class of sample selection models, in which variables are added to the dispersion and correlation parameters. A Monte Carlo simulation study is performed to assess the behavior of the parameter estimation method. Two real data sets are analyzed to illustrate the proposed approach.
