Invariance Properties of Statistical Procedures for Network Structures Identification
Invariance properties of statistical procedures for threshold graph identification are considered. An optimal procedure in the class of invariant multiple decision procedures is constructed.
Identification of network structures using the finite-size sample has been considered.
The concepts of random variables network and network model, which is a complete weighted
graph, have been introduced. Two types of network structures have been investigated: network
structures with an arbitrary number of elements and network structures with a fixed number
of elements of the network model. The problem of identification of network structures has
been investigated as a multiple testing problem. The risk function of statistical procedures for
identification of network structures can be represented as a linear combination of expected
numbers of incorrectly included elements and incorrectly non-included elements. The sufficient
conditions of optimality for statistical procedures for network structures identification with
an arbitrary number of elements have been given. The concept of statistical uncertainty of
statistical procedures for identification of network structures has been introduced.
Emotional intelligence is regarded as one of the most important professional competencies of managers and specialists in human resources services. The efficiency of the whole company depends on the extent to which an HR manager is able to identify his or her emotions, the emotions of clients and employees, and also the way he or she uses them to make decisions. The developed emotional intelligence of the HR manager is the basis for attracting new partners, building long-term partnerships, strengthening the favorable social and psychological climate within the company. The article presents the results of the study of emotional intelligence of students studying Human Resources Management at the Higher School of Economics and the "network" model of its development in the learning process. The study involved 78 students: 56 people (71.79% of the total sample) were undergraduate students and 22 people (28.21% of the total sample) were master level students. Undergraduate students did not have any professional experience; experience of students of the Master's degree program was from 1 to 5 years in the field of human resources management (18% of masters had 3-5 years of experience, professional experience of 82% of masters was less than 1 year). To measure the level of emotional intelligence we used the Test of emotional intelligence, developed by E.A.Sergienko and E. A. Hlevnaya on the basis of the theoretical model of emotional intelligence as the ability of Mayer J.D., Salovey P. and Caruso D.R., MSCEIT Questionnaire. According to the results, the average values of general emotional intelligence and the main scales of emotional intelligence are at the level of competence. The highest rate is observed on the scale of the Emotion Management (M=102.46). Lower indicators characterize the scale of the Use of emotions (M=95,61). 64.10% of the respondents are characterized by an average level of the development of the ability to understand and rule emotions; 56.41% of students have a high level of development of the ability to perceive, identify their emotions and emotions of others; 38.46% have a low level of development of the ability to use emotions to solve problems. The growth of indicators of emotional intelligence from Bachelor to Master in all branches of emotional intelligence is noted. A "network model" of the organization of educational disciplines and additional resources for developing the emotional intelligence of future HR managers is proposed.
This book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.
The main goal of the present paper is the development of a general framework of multivariate network analysis of statistical data sets. A general method of multivariate network construction, on the basis of measures of association, is proposed. In this paper we consider Pearson correlation network, sign similarity network, Fechner correlation network, Kruskal correlation network, Kendall correlation network, and the Spearman correlation network. The problem of identification of the threshold graph in these networks is discussed. Different multiple decision statistical procedures are proposed. It is shown that a statistical procedure used for threshold graph identification in one network can be efficiently used for any other network. Our approach allows us to obtain statistical procedures with desired properties for any network. © 2015 Springer International Publishing Switzerland.
Market network analysis attracts a growing attention last decades. One of the most important problems related with it is the detection of dynamics in market network. In the present paper, the stock market network of stock’s returns is considered. Probability of sign coincidence of stock’s returns is used as the measure of similarity between stocks. Robust (distribution free) multiple testing statistical procedure for testing dynamics of network is proposed. The constructed procedure is applied for German, French, UK, and USA market. It is shown that in most cases where the dynamics is observed it is determined by a small number of hubs in the associated rejection graph.