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. derive estimators parameters using modified maximum likelihood methodology and explore properties so obtained. show that proposed are more efficient robust than commonly used least square estimators. also develop relevant test hypothesis procedures compared performance such tests vis-a-vis classical based upon approach.