FITTING: A SUBROUTINE TO FIT FOUR-MOMENT PROBABILITY DISTRIBUTIONS TO DATA

Fitting is a Fortran subroutine that constructs a smooth, generalized four-parameter probability distribution model. It is fit to the first four statistical moments of the random variable X (i.e., average values of X, X2, X3, and X4). This distribution is said to be "generalized" in that it generalizes various conventional, standard two-parameter "parent" distribution models. The user may select here between Gaussian, Gumbel, or Weibull parent models. The generalized model is then produced from a cubic distortion of the parent model, calibrated to match the first four moments of the data. (Depending on the numerical values of the moments, an inverse cubic distortion is instead used in some cases.) This four-moment matching is intended to provide models that are more faithful to the data in the tail regions of interest.

Publication Details

Journal
Stanford Digital Repository
Published
2025-05-20
DOI
https://doi.org/10.25740/br338gb8343
Primary Topic
Statistical Methods and Bayesian Inference
Type
article
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article

FITTING: A SUBROUTINE TO FIT FOUR-MOMENT PROBABILITY DISTRIBUTIONS TO DATA

Stanford Digital Repository
Statistical Methods and Bayesian Inference
article

FITTING: A SUBROUTINE TO FIT FOUR-MOMENT PROBABILITY DISTRIBUTIONS TO DATA

article en

Abstract

Fitting is a Fortran subroutine that constructs a smooth, generalized four-parameter probability distribution model. It is fit to the first four statistical moments of the random variable X (i.e., average values of X, X2, X3, and X4). This distribution is said to be "generalized" in that it generalizes various conventional, standard two-parameter "parent" distribution models. The user may select here between Gaussian, Gumbel, or Weibull parent models. The generalized model is then produced from a cubic distortion of the parent model, calibrated to match the first four moments of the data. (Depending on the numerical values of the moments, an inverse cubic distortion is instead used in some cases.) This four-moment matching is intended to provide models that are more faithful to the data in the tail regions of interest.

Stanford Digital Repository
Openalex Percentile: Top 59%
Statistical Methods and Bayesian Inference
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