Published

2014-07-01

Graphical Tools to Assess Goodness-of-Fit in Non-Location-Scale Distributions

Herramientas gráficas para evaluar bondad de ajuste en distribuciones de no-localización-escala

Keywords:

Censored Data, Confidence Band, Data Analysis, Probability Plots (en)
Análisis de datos, Bandas de confianza, Datos censurados, Gráficos de probabilidad. (es)

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Authors

  • Claudia Castro-Kuriss Instituto Tecnológico de Buenos Aires, Argentina
  • Víctor Leiva Universidad Adolfo Ibáñez, Chile Universidad de Valparaíso, Chile
  • Emilia Athayde Universidade do Minho, Portugal

Goodness-of-fit (GOF) techniques are used for assessment whether a distribution is suitable to describe a data set or not. These techniques have been studied for distributions belonging to the location-scale family. However, one could be interested in making this assessment for distributions that do not belong to this family. We review the available GOF tests and propose graphical tools based on these tests for censored and uncensored data from non-location-scale distributions. Anderson-Darling, Cramér-von Mises, Kolmogorov-Smirnov, Kuiper, Michael and Watson GOF statistics are considered. We apply the proposed results to real-world data sets to illustrate their potential, with emphasis on some Birnbaum-Saunders distributions.

Las técnicas de bondad de ajuste se usan para establecer si una distribución es apropiada o no para describir un conjunto de datos. Estas técnicas han sido estudiadas para distribuciones pertenecientes a la familia de locación y escala. Sin embargo, podríamos también estar interesados en establecer si una distribución que no pertenece a esta familia brinda un buen ajuste a los datos. Revisamos los tests de bondad de ajuste disponibles y proponemos herramientas gráficas basadas en estos tests para datos completos y censurados desde distribuciones de no locación y escala. Consideramos los estadísticos de Anderson-Darling, Cramér-von Mises, Kolmogorov-Smirnov, Kuiper, Michael y Watson. Aplicamos los resultados propuestos a conjuntos de datos reales para ilustrar su potencial, particularmente en algunas distribuciones Birnbaum-Saunders

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