Graphical Tools to Assess Goodness-of-Fit in Non-Location-Scale Distributions
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...
- Autores:
-
Castro-Kuriss, Claudia
Leiva, Víctor
Athayde, Emilia
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2014
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/66559
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/66559
http://bdigital.unal.edu.co/67587/
- Palabra clave:
- 51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Censored Data
Confidence Band
Data Analysis
Probability Plots
Análisis de datos
Bandas de confianza
Datos censurados
Gráficos de probabilidad.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | 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. |
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