Linked Questions

9
votes
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 ...
6
votes
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: ...
9
votes
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 ...
20
votes
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 ...
11
votes
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 ...
21
votes
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 ...
1
vote
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 ...
2
votes
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}(...
2
votes
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 ...
0
votes
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 ...
1
vote
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 ...