Linked Questions

11 questions linked to/from Standard errors for lasso prediction using R
1answer
5k views

How does glmnet handle overdispersion?

I have a question about how to model text over count data, in particular how could I use the lasso technique to reduce features. Say I have N online articles and ...
1answer
6k views

How to obtain Confidence Intervals for a LASSO regression?

I'm very new from R. I have this code for a LASSO regression: ...
2answers
7k views

LASSO Regression - p-values and coefficients

I've run a LASSO in R using cv.glmnet. I would like to generate p-values for the coefficients that are selected. I found the boot.lass.proj to produce bootstrapped ...
2answers
734 views

Does LASSO suffer from the same problems stepwise regression does?

Stepwise algorithmic variable-selection methods tend to select for models which bias more or less every estimate in regression models ($\beta$s and their SEs, p-values, F statistics, etc.), and are ...
1answer
986 views

How to interpret variables that are excluded from or included in the lasso model?

I got from other posts that one cannot attribute 'importance' or 'significance' to predictor variables that enter a lasso model because calculating those variables' p-values or standard deviations is ...
2answers
743 views

confidence intervals' coverage with regularized estimates

Suppose I'm trying to estimate a large number of parameters from some high-dimensional data, using some kind of regularized estimates. The regularizer introduces some bias into the estimates, but it ...
1answer
323 views

Deal with NA's in power transformed data

I'm running a LASSO regression following this guide. I pre - processed my dependent variable using a simple power transformation to obtain a standard normal distribution. Unfortunately, this means I ...
1answer
357 views

Performance of linear least squares regression subject to inequality (bounded interval) constraints on parameters

Consider the following model: $${\bf y} = {\bf X}{\bf b} + {\bf e}$$ where ${\bf y}, {\bf n}\in {\cal R}^m$, ${\bf b}\in{\cal R}^n$, and ${\bf X}\in{\cal R}^{m \times n}$ where \$m>n = {\rm rank}(...
0answers
202 views

Where does the standard error come from in Lasso?

I'm using Lasso to get the best variables to predict an outcome. I understand that this graph gives me λmin and λ1se However, I don't understand how to get that standard error theoretically. Where ...
1answer
70 views

Is there any need for regularization in an overdetermined multiple regression problerm?

Supposed I have a small number of features, say 4 or 5, and I have hundreds of data points. That is, I am in an over-determined situation. Is there any benefit to using regularization in this setting ...
1answer
93 views

How to calculate a confidence interval in R for a binomial mixed-effect model (which was fit using the R package glmmLasso)?

How does one calculate confidence intervals for a binomial mixed-effect model that was fit using the R package glmmLasso? I am interested in the 95% confidence intervals for the fixed effects. confint ...