Open AccessInference in Multiple Linear Regression Model with Generalized Secant Hyperbolic Distribution Errors
Author(s) -
Álvaro Alexander Burbano Moreno,
Óscar Orlando Melo Martínez,
M. Qamarul Islam
Publication year - 2021
Publication title -
ingeniería y ciencia/ingeniería y ciencia
Language(s) - English
Resource type - Journals
eISSN - 2256-4314
pISSN - 1794-9165
DOI - 10.17230/ingciencia.17.33.3
Subject(s) - estimator , mathematics , inference , maximum likelihood , m estimator , linear regression , statistics , statistical inference , generalized linear model , computer science , artificial intelligence
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.