The supplement provides a detailed proof of Theorem 4.3, which is the lower bound of the
misclassification error for the high-dimensional QDA problem with sparsity assumptions, and …

In this paper, we develop a systematic theory for high-dimensional analysis of variance in
multivariate linear regression, where the dimension and the number of coefficients can both …

Estimation and inference for precision matrices of nonstationary time series Page 1 The Annals
of Statistics 2020, Vol. 48, No. 4, 2455–2477 https://doi.org/10.1214/19-AOS1894 © Institute of …

This study develops a simple method for constructing confidence bands centered at a
principal component analysis (PCA)-based estimator of the slope function in a functional …

This paper establishes consistency of the weighted bootstrap for quadratic forms \left(n^-
1/2i=1^nZ_i,n\right)^T\left(n^-1/2i=1^nZ_i,n\right) where (Z_i,n)_i=1^n are mean zero …

Testing differences in mean vectors is a fundamental task in the analysis of high-
dimensional compositional data. Existing methods may suffer from low power if the …

When dealing with very high-dimensional and functional data, rank deficiency of sample
covariance matrix often complicates the tests for population mean. To alleviate this rank …

High dimensional tensor time series data is increasingly prevalent across various fields. In
the analysis of such data, factor modelling plays a crucial role as a dimension reduction tool …

The paper introduces a new test for testing structures of covariances for high dimensional
vectors and the data dimension can be much larger than the sample size. Under proper …

The metagenome embedded in urban sewage is an attractive new data source to
understand urban ecology and assess human health status at scales beyond a single host …