Timeline for Adaptive LASSO, confidence interval and sample size
Current License: CC BY-SA 4.0
4 events
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| Mar 27, 2022 at 17:55 | comment | added | EdM |
@hela there's no simple, quick answer to your quick question. For the complexities, see Statistical Learning with Sparsity; Chapter 6 covers confidence intervals etc., which aren't straightforward with lasso. Combining continuous and categorical predictors in lasso is OK if you make proper decisions about scaling them. Bootstrapping simulates repeated sampling from the population. That's a standard validation method for clinical models, e.g. in the validate() function of the rms package.
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| Mar 27, 2022 at 17:33 | vote | accept | hela | ||
| Mar 27, 2022 at 17:31 | comment | added | hela | Million thanks EdM!! There is so much useful information that you gave on this answer that I have to go and train myself for Just a quick question. So overall you say there is no problem to use adaptive lasso but instead of having a train and test data, I can validate the model by running the lasso on multiple bootstraps (is it a simulation? Does that make sense when I am working with real data) because we have clinical data and so this is basically an analysis on 200 units who are patients with a specific health condition. Also, about group lasso, can I have a mix of continuous and categ? | |
| Mar 26, 2022 at 17:43 | history | answered | EdM | CC BY-SA 4.0 |