Timeline for Help in Extending Least squares for sparse coefficients
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
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| May 6, 2017 at 16:45 | history | bounty awarded | CommunityBot | ||
| May 4, 2017 at 18:53 | comment | added | H. Rev. | Well, normally you would normalize your MSE differently if you have more or less predictors (e.g. (y.^2-ypred.^2)/(n-L) where n is number of samples and L number of predictors) otherwise you cannot really compare (see here) then, Yes: it is absolutely normal to get such high values, since the variance of your y is roughly of that order (1e5). Plot y(iHo) and yLasso together, they're almost identical... If you want a measure of accuracy that is more intuitive use the coefficient of determination (pretty much a percent..) | |
| May 4, 2017 at 17:31 | vote | accept | SKM | ||
| May 4, 2017 at 17:31 | comment | added | SKM | When I decrease the lag to say `L=10' then the MSE is lower. But for higher lags the MSE performance is so high. Can you please exxplain this? | |
| May 4, 2017 at 17:08 | comment | added | SKM | Than you for the revised code. I ran the updated code but the MSE is very high : MSE LASSO with Intercept: 99910.739554 MSE LASSO without Intercept: 88108.648107 I don't think MSE would be that high. Did you get this high MSE? | |
| May 4, 2017 at 15:37 | history | edited | H. Rev. | CC BY-SA 3.0 | added 2068 characters in body |
| May 4, 2017 at 15:00 | comment | added | H. Rev. | I think you got a bit confused, first why don't you use the first column of X1 (by calling lasso on X1(iTr,2:end))? Finally as I said you will have NaN in X1 (for all the rows where negative lags do not exist! i.e. first 88 rows for numLags=89), so when you compute the prediction yLasso, you might have some NaN left, then the MSE, which calls mean will be NaN. Try using nanmean | |
| May 3, 2017 at 18:08 | comment | added | SKM | Thank you for your reply, a lot of concepts are cleared based on your explanation. However, there are 2 things which I could not follow properly - (1) how to correclty apply the estimates corresponding to the lowest MSE (2) how to use cross validation and evaluate the MSE performance. I have extended your code to a larger MA model presented uder the Update part of the Question. I am getting incorrect result. Could you please help in correctly applying the estimated coefficients so that I can obtain the MSE between y and yLASSO? | |
| May 3, 2017 at 14:50 | history | edited | H. Rev. | CC BY-SA 3.0 | added 79 characters in body |
| May 3, 2017 at 14:43 | history | answered | H. Rev. | CC BY-SA 3.0 |