Selective inference using randomized group lasso estimators for general models

arXiv:2306.13829v3 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv categorical class count data example family functions general inference lasso likelihood loss math.st modeling stat.me stat.ml stat.th type