Jagannath, Rakshith; Upadhye, Neelesh S. The Lasso estimator: distributional properties. (English) Zbl 1449.62162 Kybernetika 54, No. 4, 778-797 (2018). Summary: The least absolute shrinkage and selection operator (LASSO) is a popular technique for simultaneous estimation and model selection. There have been a lot of studies on the large sample asymptotic distributional properties of the LASSO estimator, but it is also well-known that the asymptotic results can give a wrong picture of the LASSO estimator’s actual finite-sample behaviour. The finite sample distribution of the LASSO estimator has been previously studied for the special case of orthogonal models. The aim in this work is to generalize the finite sample distribution properties of LASSO estimator for a real and linear measurement model in Gaussian noise.In this work, we derive an expression for the finite sample characteristic function of the LASSO estimator, we then use the Fourier slice theorem to obtain an approximate expression for the marginal probability density functions of the one-dimensional components of a linear transformation of the LASSO estimator. Cited in 1 Document MSC: 62J07 Ridge regression; shrinkage estimators (Lasso) 62J05 Linear regression; mixed models 62G05 Nonparametric estimation 60E05 Probability distributions: general theory Keywords:linear regression; Lasso; characteristic function; finite sample probability distribution function; Fourier-Slice theorem; Cramer-Wold theorem Software:CVX PDFBibTeX XMLCite \textit{R. Jagannath} and \textit{N. S. Upadhye}, Kybernetika 54, No. 4, 778--797 (2018; Zbl 1449.62162) Full Text: DOI arXiv