Standard assumptions incorporated into Bayesian model selection procedures result in
procedures that are not competitive with commonly used penalized likelihood methods. We …

Background The study of circulating biomarkers and their association with disease
outcomes has become progressively complex due to advances in the measurement of these …

This paper establishes non-asymptotic oracle inequalities for the prediction error and
estimation accuracy of the LASSO in stationary vector autoregressive models. These …

We introduce a very general method for high dimensional classification, based on careful
combination of the results of applying an arbitrary base classifier to random projections of …

Statistical inference for model parameters in stochastic gradient descent Page 1 The Annals of
Statistics 2020, Vol. 48, No. 1, 251–273 https://doi.org/10.1214/18-AOS1801 © Institute of …

Lasso is a widely used regression technique to find sparse representations. When the
dimension of the feature space and the number of samples are extremely large, solving the …

Summarizing the effect of many covariates through a few linear combinations is an effective
way of reducing covariate dimension and is the backbone of (sufficient) dimension …

The varying coefficient model is an important class of nonparametric statistical model, which
allows us to examine how the effects of covariates vary with exposure variables. When the …

This article is concerned with feature screening and variable selection for varying coefficient
models with ultrahigh-dimensional covariates. We propose a new feature screening …

This paper reviews predictive inference and feature selection for generalized linear models
with scarce but high-dimensional data. We demonstrate that in many cases one can benefit …