Model order selection using Least squares

This is a simulation of order recurssive algorithm on how to find the model order of an estimator.

Always a linear estimator is something that is acheivabl and an estimator designed in linear model is effictive. Many types of estimators are pressent, such as MVUE, MLE's etc but the Least Squares estimator is something that works on the error signal than rather working arund the statistical model to achieve the CRLB, by minimising the variance. Here the code tries to model a signal corrupted with noise and an efficient estimator is that which keeps the model simple. As the order of the model increases the Jmin doesn't decrese effictively and beyind a certian point we start fitting noise. The code tries to achieve that optimal order by analysing the Jmin Vs K(model Order) curve.