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09-23-2016 06:09 PM

Hello Everyone,

I want to demonstrate that

a) LASSO regression is superior to stepwise selection for variable selection

b) LASSO regression is superior to linear regression for prediction

I would like to use PROC GLMSELECT in SAS 9.3 to illustrate this. Would anyone have a data set and some code to do so?

If you have just the data set but no code, that's fine - I would be glad to write it by myself.

If you have both the data and the code, that would be even better!

Thanks for your help.

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09-23-2016 10:54 PM

Compare AIC BIC PRESS .... these model fit statistic with these two method. I don't understand your second question. LASSO is variable selection method ,not a regression method. There are some better method than LASSO , like Net-LASSO . Check the documentation .

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09-28-2016 03:42 PM

Hi Ksharp,

Sorry - I could have phrased that second question better. Suppose I generate 2 different models:

a) one model is obtained from stepwise selection

b) one model is obtained from LASSO

I want to show that the predictive accuracy of Model B is higher than that of Model A.

As Wikipedia notes, LASSO enhances the predictive accuracy of a resulting statistical model.

https://en.wikipedia.org/wiki/Lasso_(statistics)

Would you have an example data set that I can use to demonstrate this?

Thanks.

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09-29-2016 04:31 AM

Yeah. LASSO would be better than STEPWISE . After using these two method with PROC GLMSELECT , Check the following fit statistics: AIC 2172.72685 AICC 2174.27787 SBC 1736.94624 ASE (Train) 24.18515 ASE (Validate) 25.74617 ASE (Test) 22.57297 I would expect LASSO has smaller value of these statistics than STEPWISE.

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10-03-2016 11:07 AM

Hello Ksharp,

Could you please tell me where you got these statistics? Did you apply those methods to a data set? If so, could you please tell me where that data set comes from?

Thanks.

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10-03-2016 10:36 PM

These goodness-fit statistics I referred to is from SAS documentation. There are many example you can work with in PROC GLMSELECT documentation.