A statistical approach to life quality analysis
The article deals with the approaches to the study of population life quality in countries around the world. We considered the formation of the main Human Development Index, caalculated annually by the United Nations, its composition and method of calculating the indicators, changes in the structure and calculation of the indicators of the HDI. According to the data of the 2014 United Nations Report, all countries of the world were divided into four groups: the countries with very high human development level, the countries with high human development level, the countries with medium human development level and the countries with low human development level. In the paper the complex analysis of human development is conducted for the group with very high level of development. In the study we suggested the method of selection of integrated, generalizing indicators using the method of dimension reduction – the method of principal components. By the method of principal components the generalizing integrated indicators, which allowed assessing living conditions of the population at the most generalized level, were identified. The principal factors were selected taking into account the total accumulated dispersion, Kaiser criterion and the scree plot. In the result of the recalculations with the axes rotation by the method of Varimax of initials, three principal components were obtained. The model of dependance of the Human Development Index (HDI) – y on the selected principal factors f1, f2 and f3 was built. The countries, included to the first group with very high human development level were classified using cluster analysis. The classification was carried out by the selected generalizing factors. In the result of hierarchical procedures for combining, we identified four groups of countries – four clusters, which confirm the differences in the conditions of human development in the group of countries with very high human development level. © MCSER-Mediterranean Center of Social and Educational Research.
In recent decades, the interest in research emerging markets is actively growing. Companies in emerging markets (such as Russia) copy the business models of companies in developed markets, but this behaviour does not lead to the same results. Companies should use a different path of development, thus researches goal is to highlight the features of advanced and emerging countries. In this work we study marketing in the Russian market. The paper examines the state of marketing practices in Russia with the help of the international project methodology Contemporary marketing practices (CMP), developed at Auckland University. This methodology allows to describe and classify existing market practices. The instrument of investigation is CMP’s questionnaire, which was translated into Russian. The questionnaire examines the company to one of the types of marketing: transaction marketing, database marketing, interactive, internet marketing and network, or to the intersection of these types. The paper describes existing practices in the Russian market by cluster analysis. Moreover, the paper presents cross-country comparison among CMP’s studies. In this paper, there is no explanation of the reasons for these results, but it puts forward a number of hypotheses to be tested in the future study.
We present a complex analysis of business models for large, medium and small Russian commercial banks from 2006 to 2009. The Russian banks are grouped based on homogeneity criteria of their financial and operational outcomes. The banks’ structure of assets and liabilities, profitability and liquidity ratio are taken into account. The results show how the banks are adjusted their business models before and after the financial turmoil taken place in 2008. In addition, the prevailing banking business models observed for the leading banks in Russia are defined. The banks often changing their business models are found and analyzed.
The relevance of the chosen topic is closely related to the course of the demographic policy of the state until 2024 to increase life expectancy and reduce the poverty level of the population. World experience shows that climatic conditions, living conditions, the quality of food and drinking water, alcohol consumption are important components of public health and life expectancy in general. Despite the positive dynamics of life expectancy over the past decade, Russia still has a huge regional differentiation (16,6 years for women, 18,2 years for men in 2016) and an average gender gap in Russia - 10 ,6 years. The paper examines the problems of regional differentiation of life expectancy at birth with consideration of gender specifics in the context of the lifestyle and living conditions of the population.
According to Rosstat 2016, a typology of the regions on the life expectancy of men and women by the K-means method into 3 clusters was carried out, similar problems of the regions were identified in terms of alcohol consumption, quality of food and drinking water, living conditions. The regions of the 1st cluster (Yevreyskayaavtonomnaya oblast, RespublikaTyva, Chukotskiyavtonomnyyokrug) require special attention. There are the lowest rates of life expectancy for men and women, poor food quality, high alcohol consumption, high mortality rates from external causes, poor housing conditions and poor quality of drinking water.
On the basis of the obtained results, possible reserves for reducing the gender and regional differentiation of life expectancy were analyzed. The analysis performed and the methodology used will contribute to the development of a system for monitoring the implementation of the May presidential decrees (2018) in order to increase life expectancy and improve the quality of life of the population.
The central focus of this paper is a methodological one. Using the set of indicators of state capacity, we demonstrate a specific strategy for identifying sustainable structures in multidimensional data sets that reflect complex and ambiguous concepts of political science. A key feature of this strategy is the application of related, but significantly different technically, multidimensional methods – cluster and pattern analyses. We use hierarchical clustering with various combinations of metrics and amalgamation rules, as well as ordinal-invariant pattern-clustering. Properties of pattern analysis as a method for studying multidimensional data are shown for the first time (to the best of our knowledge) in the political science literature. Since clustering has been actively used in political science for a long time, pattern analysis is still practically not adopted in our science. This situation requires correction, since pattern-analysis has some important and in many ways unique capabilities. It was shown that the combination of pattern and cluster analyses makes it possible to identify consistent structures that have a clear interpretation in terms of political science. Thus, in the course of our study, several types of state capacity were identified (although this task was rather illustrative for us). We use a set of empirical indicators of state capacity: the share of military spending in GDP, the share of military personnel in the total population, the share of tax revenues in GDP, the total rate of homicides and victims of internal conflicts, and the quality of government institutions. Data for more than 150 countries are taken for 1996, 2005 and 2015. Stable combinations of the values of these indicators, identified simultaneously via pattern and cluster analyses, form the structures of state capacity. In conclusion, we show the most promising directions for the development of the methodology described in this paper. One of the most important is the analysis of the dynamics of countries within the pattern-cluster structures of state capacity.
The article examines the literature, which determinates the factors affecting Gross Regional Product as well as broadens the analysis to different regions of the Russian Federation. The regressions modeling and cluster analysis is used for the issue. Two linear regression models are constructed based on the indicators of the Federal Statistics Survey databases for the year 2015 as well as the index of readiness of regions of Russia for the information society (ICT) is used. After the estimation the two clusters based on the values of sub-indexes were found, presenting the typical and atypical behavior towards values of sub-indexes.
We present the basic properties of the a new pattern analysis method in parallel coordinates; results of the method do not depend on the ordering of data in the original sample of objects being analyzed. We prove that clusters obtained with this method do not overlap. We also show the possibility of representing objects of one cluster in the form of monotonically increasing/decreasing functions.
Data Correcting Algorithms in Combinatorial Optimization focuses on algorithmic applications of the well known polynomially solvable special cases of computationally intractable problems. The purpose of this text is to design practically efficient algorithms for solving wide classes of combinatorial optimization problems. Researches, students and engineers will benefit from new bounds and branching rules in development efficient branch-and-bound type computational algorithms. This book examines applications for solving the Traveling Salesman Problem and its variations, Maximum Weight Independent Set Problem, Different Classes of Allocation and Cluster Analysis as well as some classes of Scheduling Problems. Data Correcting Algorithms in Combinatorial Optimization introduces the data correcting approach to algorithms which provide an answer to the following questions: how to construct a bound to the original intractable problem and find which element of the corrected instance one should branch such that the total size of search tree will be minimized. The PC time needed for solving intractable problems will be adjusted with the requirements for solving real world problems.
This paper presents a pattern behavioral analysis of 100 largest Russian commercial banks by total assets during an eight- year period: from the first quarter of 1999 to the second quarter of 2007. Bank performance indicators are analyzed. Structural similarities in the development of the banks are examined. A cluster analysis is applied to determine banks with a similar structure of operations. This analysis allows to estimate how the structure of the Russian banking system has been changing over time. In particular, it allows to identify prevailing patterns in the behavior of Russian commercial banks and to analyze the stability of their position in a particular pattern.
In the paper are presented research results on the application of cluster analysis for the formation of a securities portfolio. There are identified features of cluster analysis applicating various indicators characterizing shares: share prices, share prices movement, and various market multipliers of issuers. It is shown the application possibility of cluster analysis in the Robo-Advisors' algorithm.