The paper deals with the estimation of a high dimensional covariance with a conditional
sparsity structure and fast diverging eigenvalues. By assuming a sparse error covariance …

Robust rank correlation based screening Page 1 The Annals of Statistics 2012, Vol. 40, No. 3,
1846–1877 DOI: 10.1214/12-AOS1024 © Institute of Mathematical Statistics, 2012 ROBUST …

Determining the number of common factors is an important and practical topic in high-
dimensional factor models. The existing literature is mainly based on the eigenvalues of the …

Variable selection is a challenging issue in statistical applications when the number of
predictors p far exceeds the number of observations n. In this ultrahigh dimensional setting …

We study a marginal empirical likelihood approach in scenarios when the number of
variables grows exponentially with the sample size. The marginal empirical likelihood ratios …

This article proposes a model-free and data-adaptive feature screening method for ultrahigh-
dimensional data. The proposed method is based on the projection correlation which …

This paper studies model selection consistency for high dimensional sparse regression
when data exhibits both cross-sectional and serial dependency. Most commonly-used …

Factor models are a class of powerful statistical models that have been widely used to deal
with dependent measurements that arise frequently from various applications from genomics …

This article is concerned with the problem of feature screening for multiclass linear
discriminant analysis under ultrahigh-dimensional setting. We allow the number of classes …

An uninformative variable elimination algorithm was combined with the Ridge regression
method. This combination makes the penalized Ridge method, which is essentially …