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Area of Science:

  • Statistics
  • Econometrics

Background:

  • Regression models with random effects are valuable for analyzing correlated data.
  • Standard models may fail with asymmetric or bimodal response variable distributions.

Purpose of the Study:

  • Propose a novel regression model with random effects at the intercept.
  • Accommodate correlated data with non-normal distributions like asymmetry or bimodality.

Main Methods:

  • Utilize the generalized inverse Gaussian distribution for modeling.
  • Employ maximum likelihood estimation for parameter estimation.
  • Conduct simulations for correlated data analysis.

Main Results:

  • The proposed model effectively handles correlated data with asymmetry or bimodality.
  • A new residual type with near-normal empirical distribution is developed.
  • The model's flexibility is demonstrated through a real-world application in land price estimation.

Conclusions:

  • The generalized inverse Gaussian regression model with random effects offers a flexible alternative for analyzing complex correlated data.
  • This contributes a valuable tool for statisticians and data analysts dealing with non-normal distributions.