Is there a way to correct standard errors and/or prediction intervals for multiple comparison after doing backwards selection?
It is well known that most model selection algorithms can easily fall into a multiple comparison trap. To quote Friedman: Consider developing a regression model in a context where substantive ...
I have two multivariate linear regression models (multiple outcomes, i.e., the responses are a matrix), and I'm measuring their performance using $R^2$ in cross-validation, over these individual ...
I hope this isn't a silly question, I'd like some advice on following up a threeway interaction in a mixed effects model. I've been building my models incrementally, like this: ...
In a longitudinal study, two groups of subjects have been measured over a period of two years at 6 months intervals. During these measurements subjects have been assessed with a series of $k$ measures ...
If a factor variable is to be dropped in model selection, should all levels be dropped simultaneously? If so, why?
In answer to a previous question factor pooling in model selection was discussed. If a factor or categorical variable is to be dropped in model selection, should all levels be dropped simultaneously? ...
I have a regularized linear regression model with a large number of parameters (~100,000) that has been optimized using a sparse fitting algorithm (coordinate descent with early stopping). The model ...