Bauman Moscow State Technical University
Author list of organization
List of articles written by the authors of the organization
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DISTRIBUTIONS OF REAL STATISTICAL DATA ARE NOT NORMAL
01.00.00 Physical-mathematical sciences
DescriptionIn the training courses on the theory of probability and mathematical statistics there are various parametric families of distributions of numerical random variables considered. Namely, we have been studying the families of normal distributions, log-normal distributions, exponential distributions, gamma distributions, Weibull-Gnedenko distributions, etc. All of them depend on one, two or three parameters. Therefore, for a complete description of the distribution it is sufficient to know or estimate one, two or three numbers. Parametric theory of mathematical statistics is widely developed, where it is assumed that the distribution of observations belong to one or another parametric family of distributions. This tradition comes from Karl Pearson, who in the early twentieth century proposed the use of four parametric family of distributions. The above families of distributions - are the subsets of a four-parametric family of Pearson. Unfortunately, parametric families exist only in the minds of the authors of textbooks on probability theory and mathematical statistics. In real life, they are not. Therefore, modern applied statistics and econometrics mainly use non-parametric methods, in which the distribution of observations can have arbitrary form. First, on an example of a normal distribution, we are discussing the impossibility of practical use of parametric families of distributions to describe specific statistical data. We give the results of research of metrologists and estimation of convergence in limit theorems. Then we discuss how the parametric methods can use for reject outlying observations. It is very unstable the significance levels for a fixed rejection rule and the parameter of the rejection rules for a fixed level of significance. Consequently, the rejection of the classic rules of mathematical statistics is not sciencebased
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ECOLOGICAL SAFETY: UNDERGROUND NON-ENVELOPED TANKS IN PERMAFROST FOR WASTE DISPOSAL DRILLING
DescriptionThe actuality of ecological issues was realized about 50 years ago. The highlight of the ecological movement to protect the environment has been, in our estimation, the United Nations Conference on Environment and Development (Rio de Janeiro, 1992), which adopted the concept of sustainable development. After 1992 the interest in ecology of broad masses was decreased slightly, although the environmental problems are not only remained, but appeared to a greater extent. However, now there is a legal basis for their decisions. Particularly, enterprises must have a certified environmental management system; otherwise they will be unable to compete in international markets. Awareness by humanity of need for environmental protection has led, in particular, to the deployment of scientific research in the field of ecological safety studies. Therefore, we have found that it is necessary and useful to report about the research of our team on this subject. Ecological security issues are highly relevant to the energy sector, in particular for gas enterprises. As an example of the new scientific results we discuss the innovative approach to the disposal of drilling waste. The basic idea - the use of underground non-enveloped tanks in permafrost soil for disposal of drilling waste. Permafrost is typically a negative impact on economic development, but in this situation it is the determining factor for a positive role, enabling lower costs to ensure ecological safety and, consequently, improve the competitiveness of domestic enterprises in the global gas market. This article is devoted to methods of dumping drilling waste and the problems that arise in their burial place. We discuss various methods of waste disposal, their advantages and disadvantages, as well as the impact on the environment
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PRODUCTION RISKS AND ECONOMIC HAZARDS OF MODERN HIGH-TECH INDUSTRIES
DescriptionThe article shows that when the plans for the creation and production of modern high-tech innovative products for different purposes it is necessary to consider all possible risks and dangers that accompany modern innovative projects. New approaches to the planning of the development of science-intensive industries should ensure that the accounting and the management of risk situations of the financialeconomic, scientific-technical and industrialtechnological character. One of the promising directions of the domestic output of the industrial complex from its current unstable state is a strategic planning and forecasting of its activity, the development strategies should be based on an evaluation of available enterprise resources, the lack of which leads to stagnation. With active government support the company can successfully implement a strategy of stabilization and progress through cooperation and diversification of production and to make a technological breakthrough in creating high-tech products of new generation
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MATHEMATICAL METHODS IN SOCIOLOGY DURING THE LAST FORTYFIVE YEARS
22.00.00 Sociological sciences
DescriptionSociology is one of the most important social sciences. Mathematical and primarily statistical methods are effective intellectual tools of sociologists. Let us analyze the work of the author of this article on the development of statistical methods to meet the challenges of sociology. Then we give the review of development of statistical methods in Russian sociology for 45 years (1970-2015). The basic scientific events of these years, first of all, were formation of applied statistics and its basis - statistics of the non-numerical data (in sociology of 70-90% of variables have non-numerical nature). Over the last 30 years, the Russian sociology has been growing rapidly in all quantitative parameters. Clearly, the depth of investigation gives the use of advanced scientific apparatus - methodology and methods of data collection and analysis, mathematical models. In our view, a fundamental breakthrough was made in our country in the 1970s. It was then in the arsenal of Russian sociologists appeared measurement theory and fuzzy sets, mathematical methods of classification and multidimensional scaling, nonparametric statistics and statistics of non-numeric data. In subsequent decades it has been a natural development of scientific apparatus. The same mathematical and statistical methods and models can be successfully applied in various fields of science and practice. Statistical methods and models are very effective in sociological, socio-economic, managerial, technical and feasibility studies, medicine, history, in almost any industry and application areas of knowledge. Within this field, the main event of the last thirty five years - is becoming a scientific and practical discipline "Applied Statistics", dedicated to the development and application of statistical methods and models. An analysis of the dynamics of applied statistics leads to the conclusion that in the XXI century the statistics of non-numerical data is becoming a central area of applied statistics, as it contains the most common approaches and results
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01.00.00 Physical-mathematical sciences
DescriptionWe should have trained staff to implement innovative strategies. Therefore, it is natural, that a lot of attention is paid to the staffing of the management processes of innovative activity at the enterprises of the rocket and space industry (RCP). Training and human resources management in accordance with current legislation should be based on professional standards. The content of professional standards should reflect the results of forecasting scientific and technical progress in the field, for example, in the RCP. It is necessary to forecast trends in the use of information and communication technologies in solving management problems in the socio-economic sphere in order to reflect these developments in professional standards. The approach to solving this problem is the subject of this article. What should the professional standard be like in the RCP? The main problem lies in the fact that although the standard is to be enacted in the near future, its actual impact on the industry will start in 5 - 10 years and will continue for at least another 10 years, ie, until the 2030s. Professional standards should come from "Education through Science" concept, ie, knowledge, skills, competences, provided by a professional standard, should be based on modern scientific achievements. For example, mathematical methods of research should be based on a new paradigm in the area of knowledge and statistical data analysis methods must meet high statistical techniques. For the development of professional standards in the field of the RCP it is necessary to predict the characteristics of the qualification (level of knowledge, skills and experience) required the employee to carry out professional activities in the RCP in 2020 - 2030. Modern information and communication technologies are creating a fundamentally new situation in the organization of the economy. We have an ability to manage the work of organizational units, scattered throughout the world, from a single center. The requirement of presence in the workplace is mainly a relic of the past. We have a lot of advantages in a remote work
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MANAGEMENT PROBLEMS IN SMALL PRODUCTION COMPANIES AT EARLY LIFECYCLE STAGES
DescriptionIn 1970 in the journal publications of "Forbes" and "Business week" the term of "startup" appeared, which later became popular in the scientific and business literature. Startups are the organizations, which create a new product or service under conditions of high uncertainty. In the last 25-30 years, due to Russia's transition from a planned economy to the mixed, many researchers and practitioners in the field of management, economics and entrepreneurship are concerned of some questions of small business, including production. It is particularly acute problem of deaths of Russian small businesses: only three out of a hundred small businesses manage to survive for more than 3 years. In addition, one of the main reasons, why we have such statistics, is management deficiencies and administrative errors, which are studied in this article. We are primarily interested in small manufacturing plants and problems of development in the early stages of the life cycle. In the literature, it has been given just little attention. A small production company is a company associated with the production organization or incorporation of the product / technology in the production process. We regard the small production companies at an early stage of development, working in the field of mechanical engineering, instrumentation, energy, telecommunications, robotics, materials production. In this work, we analyze the first foreign and then domestic research on small business, discuss the problems of management of small industrial enterprises in the early stages of the life cycle (based on the results of our questionnaire studies) and as an example, consider the story of a startup - All-Union Center of statistical methods and Informatics of Central Board of the All-Union economic society (now - Institute of high statistical technologies and econometrics of Bauman Moscow State Technical University)
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ORGANIZATIONAL AND ECONOMIC MODELING IN SOLVING PROBLEMS OF CONTROLLING
DescriptionAt the Department of "Economics and organization of production" at the end of XX - beginning of XXI centuries created the scientific school in the field of organizational and economic modeling, econometrics and statistics. The same name section of the department oversees the teaching of the relevant disciplines. The Laboratory of economic and mathematical methods in controlling of the Research and Education Center "Controlling and innovation in management" of Bauman Moscow State Technical University conducts research in this domain. This article is devoted to the activities of the scientific school, conducting research, and some of the results. We start with a discussion of the definitions of terms, which we use. Organizationaleconomic modeling - scientific, practical and academic discipline which devoted to the development, research and application of mathematical and statistical methods and models in economics and management of the national economy, especially in economics and management of industrial enterprises and their associations. The term "economic-mathematical methods and models" has close content. Statistical methods in economics - the subject of econometrics, the base of which is applied statistics. Organizational-economic modeling and econometrics are discussed as a theoretical and practical trainings and discipline. We developed textbooks and manuals on the organizational and economic modeling, econometrics and statistics. We have conducted theoretical research and development of applications in the field of organizational and economic modeling. In particular, the prediction is regarded as one of the management functions in industry. We study the problem of stability in the models and methods of development of strategy of the enterprise. For prospective organizational and economic mechanisms of management of industrial and economic activities, we proposed design based on solidary information economy
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APPLIED STATISTICS – THE STATE AND THE PROSPECTS
01.00.00 Physical-mathematical sciences
DescriptionApplied Statistics - the science of how to analyze the statistical data. As an independent scientificpractical area it develops very quickly. It includes numerous widely and deeply developed scientific directions. Those who use the applied statistics and other statistical methods, usually focused on specific areas of study, ie, are not specialists in applied statistics. Therefore, it is useful to make a critical analysis of the current state of applied statistics and discuss trends in the development of statistical methods. Most of the practical importance of applied statistics justifies the usefulness of the work on the development of its methodology, in which the field of scientific and applied activities would be considered as a whole. We have given some brief information about the history of applied statistics. Based on Scientometrics of Applied Statistics we state that each expert has only a small part of accumulated knowledge in this area. We discuss five topical areas in which modern applied statistics develops, ie five "points of growth": nonparametric, robustness, bootstrap, statistics of interval data, and statistics of non-numerical data. We discuss some details of the basic ideas of a non-numerical statistics. In the last more than 60 years in Russia, there has been a huge gap between official statistics and the scientific community of experts on statistical methods
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01.00.00 Physical-mathematical sciences
DescriptionFuzzy sets are the special form of objects of nonnumeric nature. Therefore, in the processing of the sample, the elements of which are fuzzy sets, a variety of methods for the analysis of statistical data of any nature can be used - the calculation of the average, non-parametric density estimators, construction of diagnostic rules, etc. We have told about the development of our work on the theory of fuzziness (1975 - 2015). In the first of our work on fuzzy sets (1975), the theory of random sets is regarded as a generalization of the theory of fuzzy sets. In non-fiction series "Mathematics. Cybernetics" (publishing house "Knowledge") in 1980 the first book by a Soviet author fuzzy sets is published - our brochure "Optimization problems and fuzzy variables". This book is essentially a "squeeze" our research of 70-ies, ie, the research on the theory of stability and in particular on the statistics of objects of non-numeric nature, with a bias in the methodology. The book includes the main results of the fuzzy theory and its note to the random set theory, as well as new results (first publication!) of statistics of fuzzy sets. On the basis of further experience, you can expect that the theory of fuzzy sets will be more actively applied in organizational and economic modeling of industry management processes. We discuss the concept of the average value of a fuzzy set. We have considered a number of statements of problems of testing statistical hypotheses on fuzzy sets. We have also proposed and justified some algorithms for restore relationships between fuzzy variables; we have given the representation of various variants of fuzzy cluster analysis of data and variables and described some methods of collection and description of fuzzy data
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METHODS OF REDUCING SPACE DIMENSION OF STATISTICAL DATA
01.00.00 Physical-mathematical sciences
DescriptionOne of the "points of growth" of applied statistics is methods of reducing the dimension of statistical data. They are increasingly used in the analysis of data in specific applied research, such as sociology. We investigate the most promising methods to reduce the dimensionality. The principal components are one of the most commonly used methods to reduce the dimensionality. For visual analysis of data are often used the projections of original vectors on the plane of the first two principal components. Usually the data structure is clearly visible, highlighted compact clusters of objects and separately allocated vectors. The principal components are one method of factor analysis. The new idea of factor analysis in comparison with the method of principal components is that, based on loads, the factors breaks up into groups. In one group of factors, new factor is combined with a similar impact on the elements of the new basis. Then each group is recommended to leave one representative. Sometimes, instead of the choice of representative by calculation, a new factor that is central to the group in question. Reduced dimension occurs during the transition to the system factors, which are representatives of groups. Other factors are discarded. On the use of distance (proximity measures, indicators of differences) between features and extensive class are based methods of multidimensional scaling. The basic idea of this class of methods is to present each object as point of the geometric space (usually of dimension 1, 2, or 3) whose coordinates are the values of the hidden (latent) factors which combine to adequately describe the object. As an example of the application of probabilistic and statistical modeling and the results of statistics of non-numeric data, we justify the consistency of estimators of the dimension of the data in multidimensional scaling, which are proposed previously by Kruskal from heuristic considerations. We have considered a number of consistent estimations of dimension of models (in regression analysis and in theory of classification). We also give some information about the algorithms for reduce the dimensionality in the automated system-cognitive analysis