GTSPCA : Generalized Principal Component Analysis for Non-Stationary Vector Time Series

This function is used to segment a stationary/nonstationary multivariate series into n uncorrelated subseries. Notice that the following libraries are needed to be installed before using the GTSPCA function: library(roll);library(expm

Do informal groups threaten organizations? Comparing group conflict management styles with supervisors

This research aims to identify the role played by informal groups in organizational conflict. The existing literature mainly focuses on the effects of informal groups on the behaviors of employees, such as resisting management and disobeying instructions. However, studies that specifically measure how informal groups affect the behaviors of their members in handling conflicts with supervisors are lacking. This research uses quantitative methodology. Data were collected using the Rahim Organizational Conflict Inventory II survey. The participants were 316 workers in various American organizations. The results were analyzed using multivariate analysis of variance, one-way analysis of variance, Pearson’s correlation coefficient, and the two-samples z-test. The results show that employees who belong to informal groups use the dominating style more frequently than do employees who do not belong to informal groups. However, they do not always use dominating styles; occasionally, they tend to use compromising and integrating styles as well. Age has a significant impact on the relationship between informal groups and integrating and dominating styles. There is also a relationship between gender and avoiding style among employees who belong to informal groups. However, there is no preference for a certain conflict style among the three types of informal groups. The results have implications for management science, including human resources and organizational behavior. However, the research applications may be limited for employees in collectivist societies that are different from American (an individualistic society). The relationship between informal groups and conflict style with supervisors has not been studied before. Thus, this research focuses on not only the five conflict styles but also the influence of demographic variables to comprehensively understand this relationship

MpermutMax : The Maximum Moving Cross-Correlation Method

This method is a permutation method. It is used to test for significant correlations between the variables of both stationary and non-stationary multivariate time series. This method extended the Maximum Cross-Correlation methodof Change et al. (2018) to account for non-stationary high-dimensional time series. Notice that the following library is needed to be installed before using the mpermutMax function: library(roll

QMDPCA : Quadratic Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series

This function reduce the dimension of non-stationary (and stationary) multivariate time series by performing eigenanalysis on the quadratic moving cross-covriance matrix of the extended data matrix up to some specified lag. Notice that the following libraries are needed to be installed before using the MDPCA function: library(roll); library(expm)

macf : Moving Auto- and Cross-correlation Function

The function macf computes (and by default plots) estimates of the moving auto- and cross-correlation matrix of non-stationary (and stationary) time series. Notice that the following library is needed to be installed before using the macf function: library(roll

RCCQ : Retained Component Criterion for the Quadratic Moving Dynamic Principal Component Analysis

The RCC_QMDPCA criterion is a new tool to determine the optimal number of components (i.e. QMDPCs) to retain for the Quadratic Moving Dynamic Principal Component Analysis (QMDPCA). This criterion balances between the following two desires, reducing the dimension of the data and increasing the accuracy of the final results of QMDPCA; See Alshammri and Pan (2020). Notice that the following libraries are needed to be installed before using the mcov function: library(roll); library(QMDPCA

Dimension reduction for stationary multivariate time series data

Chang et al. (2016) extended PCA by finding a linear transformation of the original variables such that the transformed series is segmented into uncorrelated subseries with lower dimensions. This method is called TS-PCA. In our current research, we will extend TS-PCA by reducing the dimension of the transformed subseries further by applying GDPCA by Pena and Yohai (2016) to the results from TS-PCA, and possibly reach a further dimension reduction. Hence, the proposed method is a combination of TS-PCA and GDPCA

MDPCA : Moving Dynamic Principal Component Analysis for Non-Stationary Multivariate Time Series

This function reduce the dimension of non-stationary (and stationary) multivariate time series by performing eigenanalysis on the moving cross-covriance matrix of the extended data matrix up to some specified lag. Notice that thefollowing libraries are needed to be installed before using the MDPCA function: library(roll); library(expm)

mcov : Moving Cross-covariance Matrix

The function mcov computes estimates of the lag l moving cross-covariance matrix of non-stationary (and stationary) time series. Notice that the following library is needed to be installed before using the mcov function: library(roll

RCCM : Retained Component Criterion for the Moving Dynamic Principal Component Analysis

The RCC_MDPCA criterion is a new tool to determine the optimal number of components (i.e. MDPCs) to retain for the Moving Dynamic Principal Component Analysis (MDPCA). This criterion balances between the following two desires, reducing the dimension of the data and increasing the accuracy of the final results of MDPCA; See Alshammri and Pan (2019). Notice that the following libraries are needed to be installed before using the mcov function: library(roll); library(MDPCA