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Details on Hansen's Skewed T

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  • ¿Algo no está bien?
#723 1 comentario 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
20/100
Tipo de issue
Documentación
Claridad
Bien especificado
Estado de actividad
Estancado
Stack tecnológico
python
Área
documentation

Línea de trabajo

Start by reading the arch package's implementation of Hansen's Skewed T distribution and compare its CDF, PPF, and moments with Hansen's paper and the other sources mentioned in the issue. Done means providing a reliable source that derives these expressions, or explaining whether a closed-form source could not be found.

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Descripción

Apologies if this is the wrong way to reach out for this topic, but I am curious on the details of Hansen's Skewed T distribution as you've implemented in the arch package. I've read his paper and some other papers in the literature, but I have not found a source that derives the PPF, CDF, and moments for the distribution.

I am working on a python package myself, and I want to include an implementation of Hansen's Skewed T. I could try to derive the CDF / PPF myself or use numerical methods (and measure the skewness / kurtosis with Monte Carlo), but I prefer a closed form expression with a reliable source if possible. I am not an academic, so I feel much more comfortable programming existing distributions than trying to derive or add to the existing math.

Obviously, you have these methods implemented, and I have seen a couple of other implementations of the Skewed T distribution floating around complete with PPF's, CDF's etc, so the derivation must be out there somewhere. If you could refer me to the paper that does this work that would be greatly appreciated!

Thanks for the good work--the Arch package is one of my favorites in the Python ecosystem, one of the few that I use regularly besides the basics (Numpy, Pandas, Scipy, Matplotlib, etc)!

(EDIT: Oops, wrote this from my work account.)

Lenguaje dominante
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Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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