  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.
