Lee Chun Yin (3035469140)
Project supervisor: Dr. Raymond W.L. WONG
In a classical linear regression setting, we often assume that the explanatory variable is nonrandom without any measurement error, and that the errors are normally distributed. However, this may not be the case in real-life applications, where measurement errors may exist, and the errors may be heavy-tailed or skewed. We use the computer simulation technique to demonstrate the impacts of non-normality in the errors-in-variables model. We present numerical results from simulations based on normal, Student's t and chi-squared distributions on the ordinary least squares and method of moments estimation of regression slope parameter and residual variance.
The final report can be found here.
The main Jupyter notebook can be found here.