Estimation and Testing in One-Way ANOVA when the Errors are Skew-Normal
We consider one-way analysis of variance (ANOVA) model when the error terms have skew- normal distribution. We obtain the estimators of the model parameters by using the maximum likelihood (ML) and the modified maximum likelihood (MML) methodologies (see, Tiku 1967). In the ML method, iteratively re...
- Autores:
-
Celik, Nuri
Senoglu, Birdal
Arslan, Olcay
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/66542
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/66542
http://bdigital.unal.edu.co/67570/
- Palabra clave:
- 51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
ANOVA
Modified Likelihood
Iteratively Reweighting Algorithm
Skew-Normal
Monte Carlo Simulation
Robustness
ANOVA
Estimación
Normal sesgada
Pruebas de hipótesis
Robustez.
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | We consider one-way analysis of variance (ANOVA) model when the error terms have skew- normal distribution. We obtain the estimators of the model parameters by using the maximum likelihood (ML) and the modified maximum likelihood (MML) methodologies (see, Tiku 1967). In the ML method, iteratively reweighting algorithm (IRA) is used to solve the likelihood equations. The MML approach is a non-iterative method used to obtain the explicit estimators of model parameters. We also propose new test statistics based on these estimators for testing the equality of treatment effects. Simulation results show that the proposed estimators and the tests based on them are more efficient and robust than the corresponding normal theory solutions. Also, real data is analysed to show the performance of the proposed estimators and the tests. |
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