Selective inference using randomized group lasso estimators for general models. (arXiv:2306.13829v2 [stat.ME] UPDATED)

Selective inference methods are developed for group lasso estimators for use
with a wide class of distributions and loss functions. The method includes the
use of exponential family distributions, as well as quasi-likelihood modeling
for overdispersed count data, for example, and allows for categorical or
grouped covariates as well as continuous covariates. A randomized
group-regularized optimization problem is studied. The added randomization
allows us to construct a post-selection likelihood which we show to be adequate
for selective inference when conditioning …

arxiv categorical count data example family functions general inference lasso likelihood loss modeling