nickgeneva/kernel_smoothers

Tutorials on kernel smoothing techniques

Kernel Smoothers

Spring 2019 AME-70790 Final Project Nicholas Geneva (ngeneva at nd.edu, @NickGeneva)

Reference: Wand, M. P., & Jones, M. C. (1994). Kernel smoothing. Chapman and Hall/CRC.

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Various demo files written in python to illustrate the fundementials of kernel smoothers and kernel methods. This files were written as a part of class final project in Spring 2019.

Click on the following links to view each notebook:

1. 01_kernel_bandwidth.ipynb
2. 02_kernel_shape.ipynb
3. 03_multivariate_kernel.ipynb
4. 04_chicago_crime_density.ipynb
5. 05_local_linear_regression.ipynb
6. 06_local_quadratic_regression.ipynb
7. 07_nadaraya_watson_regression.ipynb
8. 08_multivariate_regression.ipynb
9. 09_scottish_hill_races.ipynb

Note:

If the Jupyter notebooks do not show on github you can view the rendered version at nbviewer.org. Simply paste the respective notebook url into the prompt and it will be executed.