The increasing prevalence of high-dimensional data in many scientific fields presents both
challenges and opportunities for researchers. It requires the development of new methods …

We present two examples to show how the classical multivariate statistical approaches
significantly lose efficiency or do not even work when dealing with high dimensional data …

This book comprises a collection of research contributions toward high-dimensional data
analysis. In this data-centric world, we are often challenged with data sets containing many …

High-dimensional data analysis has been a popular and attractive topic in contemporary
statistical analysis. In addition to research papers, a large body of monographs has also …

The data sets arising in modern science and engineering are often extremely large, befitting
the era of big data. But these data sets are not only large in the number of samples they …

Due to the ability of routinely collecting and storing large data sets in numerous application
areas, we have witnessed a dramatic surge of interest and activity in high-dimensional …

Analyzing the structure of a dataset is a challenging problem in high-dimensions as the
volume of the space increases at an exponential rate and typically, data becomes sparse in …

In the modern data science, analysis of high-dimensional data is attractive. One of the
important topics in high-dimensional data analysis is the detection of network structures …

In the era of big data, statistical application fields have been frequently encountering data
sets where features measured for each sample are more than sample size. In these data …

We introduce LongDat, an R package that analyzes longitudinal multivariable (cohort) data
while simultaneously accounting for a potentially large number of covariates. The primary …