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Visitors' Comments on Tutorial: KernelRegression
We have 14 comments on this tutorial
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rate up: 68 rate down: 73 last rated date: 2014-12-18
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Answer > OpenOffice (date: 2010-07-25)
By Kardi
Unfortunately Open Office does not have similar capability of MS Excel Solver
rate up: 222 rate down: 278 last rated date: 2014-12-17
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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?
rate up: 694 rate down: 190 last rated date: 2014-12-18
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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?
rate up: 174 rate down: 170 last rated date: 2014-12-15
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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
rate up: 181 rate down: 149 last rated date: 2014-12-04
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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
rate up: 168 rate down: 154 last rated date: 2014-12-10
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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.
rate up: 164 rate down: 139 last rated date: 2014-12-18
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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?
rate up: 140 rate down: 122 last rated date: 2014-12-16
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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.
rate up: 150 rate down: 116 last rated date: 2014-12-16
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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.
rate up: 166 rate down: 102 last rated date: 2014-12-16
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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.
rate up: 153 rate down: 106 last rated date: 2014-12-17
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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.
rate up: 92 rate down: 99 last rated date: 2014-12-17
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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.
rate up: 12 rate down: 12 last rated date: 2014-12-17
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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?
rate up: 10 rate down: 14 last rated date: 2014-12-18
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