Since Quetelet's work in the nineteenth century, social science has iconified the average
man, that hypothetical man without qualities who is comfortable with his head in the oven …

We propose MC+, a fast, continuous, nearly unbiased and accurate method of penalized
variable selection in high-dimensional linear regression. The LASSO is fast and continuous …

The purpose of model selection algorithms such as All Subsets, Forward Selection and
Backward Elimination is to choose a linear model on the basis of the same set of data to …

We present a path algorithm for the generalized lasso problem. This problem penalizes the l
1 norm of a matrix D times the coefficient vector, and has a wide range of applications …

We study the effective degrees of freedom of the lasso in the framework of Stein's unbiased
risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased …

We derive the degrees of freedom of the lasso fit, placing no assumptions on the predictor
matrix X. Like the well-known result of Zou, Hastie and Tibshirani Ann. Statist. 35 (2007) …

A framework is presented for generalized additive modelling under shape constraints on the
component functions of the linear predictor of the GAM. We represent shape constrained …

Classical regression methods have focused mainly on estimating conditional mean
functions. In recent years, however, quantile regression has emerged as a comprehensive …

Everyone knows about seasonality. But what exactly do we know? This study systematically
measures seasonal price gaps at 193 markets for 13 food commodities in seven African …

Isotonic regression in general dimensions Page 1 The Annals of Statistics 2019, Vol. 47, No. 5,
2440–2471 https://doi.org/10.1214/18-AOS1753 © Institute of Mathematical Statistics, 2019 …