Generalized linear models and the Lasso

Abstract

Generalized linear models build a unified framework containing many extensions of a linear model. Important examples include logistic regression for binary responses, Poisson regression for count data or log-linear models for contingency tables. Penalizing the negative log-likelihood with the l1-norm, still called the Lasso, is in many examples conceptually similar to the case with squared error loss in linear regression due to the convexity of the negative log-likelihood. This implies that the statistical properties as well as the computational complexity of algorithms are attractive. A noticeable difference, however, occurs with log-linear models for large contingency tables where the computation is in general much more demanding. We present in this chapter the models and estimators while computational algorithms and theory are described in more details in Chapters 4 and 6, respectively.