Mainstream statistical methodology is generally applicable to data observed in Euclidean
space. There are, however, numerous contexts of considerable scientific interest in which …

Principal component analysis (PCA) is used to reduce the dimensionalities of high-
dimensional datasets in a variety of research areas. For example, biological …

This paper aims to investigate how economic complexity and structural transformation affect
energy security. This study differs from previous research by focusing on energy efficiency …

A dimensionality reduction method for high-dimensional circular data is developed, which is
based on a principal component analysis (PCA) of data points on a torus. Adopting a …

This study examines the integrated sustainability perspective by mapping the spillover
effects among sustainable development pillars including social, environment, and economy …

A particularly challenging context for dimensionality reduction is multivariate circular data,
that is, data supported on a torus. Such kind of data appears, for example, in the analysis of …

Purpose Natural disasters profoundly influence agricultural trade sustainability. This study
investigates the effects of natural disasters on agricultural production imports in China within …

Over the last few decades there has been a growing interest in the use of blended or hybrid
rapid hardening cements with optimized performance for use in a variety of applications …

Abstract Principal Component Analysis (PCA) is a well-known linear dimension-reduction
technique designed for Euclidean data. In a wide spectrum of applied fields, however, it is …

One of the most common problems that any technique encounters is the high dimensionality
of the input data. This yields several problems in the subsequently statistical methods due to …