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Area of Science:

  • Statistics
  • Computational Statistics
  • Data Science

Background:

  • Simulating non-normal data with specified dependencies is challenging.
  • Existing methods like NORmal To Anything (NORTA) have limitations.
  • Flexible simulation is crucial for advanced statistical modeling.

Purpose of the Study:

  • To propose a novel and flexible simulation method for non-normal data.
  • To generalize the NORTA method using regular vines.
  • To provide a tool for specifying marginal distributions, covariance matrices, and bivariate dependencies.

Main Methods:

  • The proposed method, VIne To Anything (VITA), utilizes regular vines.
  • VITA extends the capabilities of NORTA.
  • Theoretical properties of the VITA method are derived.

Main Results:

  • The VITA method demonstrates flexibility in simulating complex data structures.
  • Two case studies illustrate the method's utility in structural equation models.
  • The study provides R code for practical implementation.

Conclusions:

  • VITA offers a powerful and adaptable approach for non-normal data simulation.
  • The method facilitates the modeling of intricate statistical relationships.
  • Availability of R code promotes wider adoption and application.