## Visitor's Comments

## Visitors' Comments on Tutorial: KernelRegression

We have 14 comments on this tutorial

Question > OpenOffice (date: 2010-07-25)

By Mike

I like Open-Office, but the solvers for it are different than those for Excel. Can you comment on using this technique with OpenOffice?

Answer > OpenOffice (date: 2010-07-26)

By Kardi

Unfortunately Open Office does not have similar capability of MS Excel Solver

Thank you > Weights (date: 2011-01-23)

By anon12345

Thank you for the excellent overview tutorial! Aside from solver, how can you find the weights?

Thank you > Kernel regression (date: 2011-05-05)

By John

I read a lot of materials to understand how the Kernel regression works.Most of them describe the theory very well which is not difficult to understand; however, I was unable to practically use the method for my regression problem. But after reading the tutorial in this website, I am very much confident that I can write my own code in R to solve the problem. Did you notice that the solver function in excel gives new values each time we optimize ? however, the results are not that much different. Thanks, John

Suggestion > Kernel Regression (date: 2011-05-16)

By Burcak Otlu Saritas

I would like to thank your tutorial on Kernel regression. I have some suggestions. First of all, font style for formulas is not good. formulas are not clear. Second, how the weight parameters are estimated in Kernel Regression (Nadaraya-Watson) is missing. Thanks, Burcak Otlu Saritas

Question > How to calculate weights (date: 2011-07-27)

By Khongor Tsogt

I just found paper that discuss about kernel density estimation and it is very useful to my study if I can use it. But, I have no knowledge about how to do kernel density estimation. So, I want to use your sample to do it my own. But, it is still not clear to understand. If it is possible can you give me description. In the internet only your example is the most easy to understand for me. Thank you.

Question > How to compute kernel (date: 2011-12-07)

By Dia

If I have the following data structure: x = [1 2 3; 3 2 4] I want to find the values form Gaussian kernel assuming that sigma = 1. All the values from kernel is z = [1 1 1; 1 1 1]; Am I correct?

Answer > kernel (date: 2011-12-13)

By Kardi

Hi Vita, In my tutorial, capital X represents your data point while small x indicates extension of that data in a very small step for the purpose of smoothing. I cannot comment on the formulations of other people. You may download the spreadsheet companion of this tutorial. It gives you example how to compute in very detail.

Others > Review (date: 2012-01-11)

By Erik Stewart

Good examples. I think the tutorial would be more helpful if you included a larger dataset and worked with a smaller subsample space (dx) to illustrate weighting and the smoothing effect across more points than just 5. Other than that, this is an exceptional introductory tutorial and is easy to understand for someone with a limited mathematical background like myself.

Suggestion > Como calculas los pesos (date: 2012-01-18)

By Hermes Herrera Martinez

Creo que pudo haberse publicado como calcular los pesos, no se como el Excel calcula, que metodo se utiliza para calcular los pesos.

Question > Weight parameters (date: 2012-08-02)

By Lazaro Martinez

How can I calculate the weights. How the weight parameters are estimated. By the way, can you help us publishing another example.

Answer > Multivariate Kernel Regression (date: 2014-08-07)

By Kardi

Thank you. For multivariate kernel regression, you can refer to the book of Hastie et al (2003) The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

Question > Kernel Regression (date: 2014-08-07)

By Leon Bezuidenhout

Good morning I found this tutorial very interesting and helped a lot with the understanding and applications. I would like to know how the spreadsheet calculation would be adjusted in the multivariate case?

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