Model order selection using Least squares
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.
Cite As
Charan Puladas (2024). Model order selection using Least squares (https://www.mathworks.com/matlabcentral/fileexchange/50096-model-order-selection-using-least-squares), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxCategories
Tags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.
Version | Published | Release Notes | |
---|---|---|---|
1.0.0.0 |