Inference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions. We derive the estimators of model parameters by using modified maximum likelihood methodology and explore the properties of the modified max...
- Autores:
-
Burbano Moreno, Álvaro Alexander
Melo-Martinez, Oscar Orlando
Qamarul Islam, M
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/30404
- Acceso en línea:
- http://hdl.handle.net/10784/30404
- Palabra clave:
- Maximum likelihood
Modified maximum likelihood
Least square
Generalized Secant Hyperbolic distribution
Robustness
Hypothesis testing
- Rights
- License
- Copyright © 2021 Álvaro Alexander Burbano Moreno, Oscar Orlando Melo-Martinez, M Qamarul Islam
| Summary: | We study multiple linear regression model under non-normally distributed random error by considering the family of generalized secant hyperbolic distributions. We derive the estimators of model parameters by using modified maximum likelihood methodology and explore the properties of the modified maximum likelihood estimators so obtained. We show that the proposed estimators are more efficient and robust than the commonly used least square estimators. We also develop the relevant test of hypothesis procedures and compared the performance of such tests vis-a-vis the classical tests that are based upon the least square approach. |
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