Estimation For Simple Linear Regression With Exponentially Distributed Errors

In general, the theory developed in the area of linear regression analysis assumes that the error ∊ is normally distributed with mean zero and variance σ². In this thesis, we examine the results when the error ∊ is exponentially distributed with scale parameter ϴ. We derive both the maximum likelihood estimate and the least square estimate and examine their important properties