It appears that you have re-discovered the fact that you can choose different regression methods, depending on the assumptions in the model (which variable, if any, are measured without error) and the quantity that you are trying to minimize. In addition to the classical least-squares regression (which minimizes the VERTICAL difference between observed and predicted Y values), there are other regression methods that minimize other quantities. Look up principal component regression, which minimizes the perpendicular distance. There are other options as well, including "partial least squares" and "reduced ridge regression," which are used when the data/experiment does not satisfy the assumptions of least squares regression. I suggest you walk over to the Statistics department at your university and have a chat with one of the statsiticans there. They can explain how the ideas in your paper related to known statistical methods.