The dynamic development of the digitized society generates large-scale information data flows. Therefore, data need to be compressed in a way allowing its content to remain complete and informative. In order for the above to be achieved, it is advisable to use the principal component method...

In this paper, we develop new methods for analyzing high-dimensional tensor datasets. A tensor factor model describes a high-dimensional dataset as a sum of a low-rank component and an idiosyncratic noise, generalizing traditional factor models for panel data. We propose an estimation algorithm,...

The importance of the components of a system has been widely studied in the literature. The main objective of such component importance measures (CIMs) is to evaluate the effect that the components can cause in the system under study by not being present or to quantify the participation of their...

This paper aims to construct a bi-directional, financial liberalization index for Pakistan by considering various financial policy indicators (reforms). This study, by employing the principal component analysis method over a period of 1980-2018 (39 years), aims to determine the composite outcome...

Steam flooding is a complex process that has been considered as an effective enhanced oil recovery (EOR) technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods while the...