The estimation of large covariance and precision matrices is fundamental in modern
multivariate analysis. However, problems arise from the statistical analysis of large panel …

Object oriented data analysis is the statistical analysis of populations of complex objects. In
the special case of functional data analysis, these data objects are curves, where a variety of …

We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a
generalized and unified asymptotic regime, which takes into account the magnitude of …

This paper introduces a Projected Principal Component Analysis (Projected-PCA), which
employees principal component analysis to the projected (smoothed) data matrix onto a …

This paper provides a characterisation of the degree of cross‐sectional dependence in a two
dimensional array,{xit, i= 1, 2,... N; t= 1, 2,..., T} in terms of the rate at which the variance of …

This paper studies the asymptotic behaviors of the pairwise angles among n randomly and
uniformly distributed unit vectors in Rp as the number of points n→∞, while the dimension p …

Supplement to “Limiting laws for divergent spiked eigenvalues and largest nonspiked
eigenvalue of sample covariance matrices”. In the Supplementary Material, we provide the …

Sparse principal component analysis has become one of the most widely used techniques
for dimensionality reduction in high-dimensional datasets. While many methods are …

Many high dimensional classification techniques have been proposed in the literature based
on sparse linear discriminant analysis. To use them efficiently, sparsity of linear classifiers is …

We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a
generalized and unified asymptotic regime, which takes into account the spike magnitude of …