Meinshausen and Buhlmann [Ann. Statist. 34 (2006) 1436–1462] showed that, for neighborhood selection in Gaussian graphical models, under a neighborhood stability condition, the LASSO is consistent, even when the number of variables is of greater order than the sample size. Zhao and Yu [(2006) J. Machine Learning Research 7 2541–2567] formalized the neighborhood stability condition in the contex...

We have developed a novel class of antisense agents, RNA Lassos, which are capable of binding to and circularizing around complementary target RNAs. The RNA Lasso consists of a fixed sequence derived from the hairpin ribozyme and an antisense segment whose size and sequence can be varied to base pair with accessible sites in the target RNA. The ribozyme catalyzes self-processing of the 5'- and ...

We consider high dimensional nonhomogeneous linear regression models with p n 9 0 or p >> n, where p is the number of features and n is the number of observations. In the model considered, the underlying true regression coefficients undergo multiple changes. Our goal is to estimate the number and locations of these change-points and estimate sparse coefficients in each of the intervals between ...

In underground excavation, where the road-headers are employed, a precise prediction of the road-header performance has a vital role in the economy of the project. In this paper, a new model is developed for prediction of the road-header performance using the non-linear multivariate regression analysis. This model is able to estimate the instantaneous cutting rate (ICR) of roadheader based on r...

The arti…cial neural network (ANN) test of Lee, White and Granger (LWG, 1993) uses the ability of the ANN activation functions in the hidden layer to detect neglected functional misspeci…cation. As the estimation of the ANN model is often quite di¢ cult, LWG suggested activate the ANN hidden units based on randomly drawn activation parameters. To be robust to the random activations, a large num...

We present a Group Lasso procedure for generalized linear models (GLMs) and we study the properties of this estimator applied to sparse high-dimensional GLMs. Under general conditions on the joint distribution of the pair observable covariates, we provide oracle inequalities promoting group sparsity of the covariables. We get convergence rates for the prediction and estimation error and we show...

The ℓ1-penalized method, or the Lasso, has emerged as an important tool for the analysis of large data sets. Many important results have been obtained for the Lasso in linear regression which have led to a deeper understanding of high-dimensional statistical problems. In this article, we consider a class of weighted ℓ1-penalized estimators for convex loss functions of a general form, including ...

Many model selection algorithms produce a path of fits specifying a sequence of increasingly complex models. Given such a sequence and the data used to produce them, we consider the problem of choosing the least complex model that is not falsified by the data. Extending the selected-model tests of Fithian et al. (2014), we construct p-values for each step in the path which account for the adapt...

We design differentially private algorithms for statistical model selection. Given a data set and a large, discrete collection of “models”, each of which is a family of probability distributions, the goal is to determine the model that best “fits” the data. This is a basic problem in many areas of statistics and machine learning. We consider settings in which there is a well-defined answer, in ...