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Birnbaum-Saunders sample selection model.

Fernando de Souza Bastos1,2, Wagner Barreto-Souza2,3

  • 1Instituto de Ciências Exatas e Tecnológicas, Universidade Federal de Viçosa - Campus UFV - Florestal, Florestal, Brazil.

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|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample selection model using the bivariate Birnbaum-Saunders distribution, addressing limitations of existing models for continuous data. The novel approach simplifies analysis and interpretation of economic and health-related outcomes.

Keywords:
Bivariate Birnbaum–Saunders distributionHeckman sample selection modelmaximum likelihood estimationnon-ignorable missing mechanismskewness

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Area of Science:

  • Econometrics
  • Statistical Modeling
  • Biostatistics

Background:

  • Sample selection bias arises when outcomes depend on unobserved selection rules.
  • Heckman's model, based on normal distribution, faces non-robustness issues.
  • Existing models often require outcome variable transformation, complicating interpretation.

Purpose of the Study:

  • Propose a novel sample selection model using the bivariate Birnbaum-Saunders distribution.
  • Develop a model that accommodates continuous outcome variables without transformation.
  • Enhance parameter interpretability in sample selection models.

Main Methods:

  • Developed a sample selection model based on the bivariate Birnbaum-Saunders distribution.
  • Employed maximum likelihood estimation for parameter estimation.
  • Conducted Monte Carlo simulation studies to assess model performance.

Main Results:

  • The proposed model maintains the same parameter count as the classical Heckman model.
  • The model accommodates continuous outcome variables directly, avoiding data transformation.
  • Empirical application demonstrated utility with ambulatory expenditures data.

Conclusions:

  • The bivariate Birnbaum-Saunders distribution offers a robust alternative for sample selection models.
  • The proposed model overcomes limitations of existing methods by handling continuous outcomes directly.
  • This approach improves the interpretability of results in applied research.