标题： 对称广义 Heckman 模型 显示英文标题

标题： Symmetric generalized Heckman models

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差问题，并且涉及响应变量的部分观测值被截断的情况。Heckman（1976）提出了一种基于双变量正态分布的样本选择模型，该模型适用于感兴趣的变量和潜在变量。最近，这种正态性假设已被更灵活的模型所放松，例如学生t分布（Marchenko和Genton，2012；Lachos等，2021）。本工作的目的是提出基于对称分布（Fang等，1990）的广义 Heckman 样本选择模型。这是一个新的样本选择模型类别，在该模型中，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。分析了两个真实数据集，以说明所提出的方法。 显示英文摘要

摘要： 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.