Scientific Journal of KubSAU

In 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)

At 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

Applied 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

Fuzzy 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

One 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

On the basis of the objective analysis it must be noted that in the arsenal of managers, especially foreign ones, there is practically no fundamentally new methods and tools of controlling. So says the executive director of Russian Association of Controllers prof. S. G. Falco. However, promising mathematical and instrumental methods of controlling actively developed in our country. It is necessary to implement them. For example, managers should be used techniques which discussed in the book by Orlov AI, Lutsenko EV, Loikaw VI "Advanced mathematical and instrumental methods of controlling" (2015). These methods are based on the modern development of mathematics as a whole - on the system interval fuzzy math (see the same named book by Orlov AI and Lutsenko EV, 2014). Considered methods are developed in accordance with the new paradigm of mathematical methods of research. It includes new paradigms of applied statistics, mathematical statistics, mathematical methods of economics, methods of analysis of statistical and expert data in management and control. In the XXI century there were more than 10 books issued, developed in accordance with the new paradigm of mathematical methods of research. The systems approach to solving specific applications often requires going beyond the economy. Very important are the procedures for the introduction of innovative methods and tools. In this article we consider the above research results in their interconnection

The relationship of Mathematical Statistics (wider - Mathematical methods of research) and history is multifaceted. In our opinion, the history of mathematical statistics is an integral part of this mathematical discipline. We have given a review of our works on the history of statistical methods. The role of mathematical statistics for the history is very important. In this article, we restrict ourselves to the questions of chronology. For centuries, the chronology is considered as a part of applied mathematics. The main problem is that the whole "common" concept of the Russian and the World history as a whole presented in textbooks was faked by the opponents of Russia after the collapse of the global Empire (Russian kingdom) in the early 17th century - 400 years ago. The stories about historical events are the information weapon. It was used by the new rulers to suppress the resistance of the vanquished. A new mathematical and statistical chronology of general and Russian history, which was built by a scientific team led by Academician Fomenko, has been helpful for the discussion about the current economic and political problems of relations between Russia and the West in the XXI century. In our opinion, the new chronology of the World and Russian history should be one of the foundations of state-patriotic ideology and deriving practical solutions. The purpose of this article is to give the initial idea of the new chronology from this point of view

We have a number of studies on the problems of the development of organizational and economic support for control tasks in the aerospace industry, primarily in the field of project management development of rocket and space technology. This article aims at summing up the preliminary results of the research cycle. Since the core funding of space activities in Russia is carried out in accordance with approved government bodies targeted programs from the state budget, among the indicators of financial and economic activities of enterprises should focus not maximize profits, and decrease costs. We must estimate of the feasibility of projects in the field of space activities, primarily on the scientific and technical feasibility and the socio-economic needs, and resource provision. What is important is the analysis of all types of resources - material, production, human resources, time, and not just financial. As a basic organizational and economic theory we suggest the use of solidary information economy, high-tech management, controlling, developed on the basis of a new paradigm of mathematical methods of economics, especially econometrics, decision theory, organizational and economic modeling. In project management to create rocket and space technology should take into account the risks of their implementation. In estimation of the feasibility of such projects there should be an analysis of risk assessment, as well as the use of modern statistical and expert methods of forecasting the dynamics of technical and economic indicators project. As practice shows, we have to develop new organizational-economic and economicmathematical models and methods. It is necessary to build a knowledge base in the art and to adequately fill it with modern knowledge based on scientific data of the Russian index of citing. In connection with the duration of the projects of development of rocket and space technology, we note the need to take account of inflation in the planning and evaluation of the financial and economic activities of enterprises, organizations and industry as a whole

In the USSR higher attestation Commission from 1975 to the collapse of the USSR was subordinated not to the Ministry of education and science, but to the Council of Ministers of the USSR directly. However, since then there is a steady trend of gradual reduction of the status of the Commission. Today it is not just included in the Ministry of education, it is just one of the units of one of its structures: the Rosobrnadzor. Reduced status of the HAC inevitably leads to a decline in the status and in the adequacy of scientific degrees assigned as well as scientific ranks. This process of devaluation of traditional academic degrees and titles assigned to the HAC, has reached the point when a few years ago there were abolished salary increments for them. Now, instead of that, every university and research institutes have developed their local, i.e. non-comparable with each other scientometric methods of evaluation of the results of scientific and teaching activities. Despite the diversity of these techniques, there is a common thing among all of them, which is the disproportionate role of the h-index. The value of the Hirsch index starts to play an important role in the protection, when considering competitive cases for positions, as well as in determining the monthly rewards for the results of scientific and teaching activities. By itself, this index is well founded, theoretically. However, in connection with the practice of its application in our conditions, in the collective consciousness of the scientific community there was a kind of mania, which the authors call the "Hirschmania". This mania is characterized by elevated unhealthy interest to the value of the Hirsch index, as well as incorrect manipulation of its value, i.e. inadequate artificial exaggeration of this value, as well as a number of negative consequences of that interest. In this study we have made an attempt to construct a quantitative measure for assessing the extent of improper manipulation of the value of the Hirsch index, and offered a science-based modification of the h-index, insensitive (resistant) to the manipulation. The article presents a technique for all the numerical calculations, which is simple enough for any author to use

We have considered the formation of the Russian scientific school in the field of econometrics, obtained its obtained scientific results, the possibilities of their use in solving problems of the economy, the organization of production and controlling of industrial companies and organizations, as well as in teaching. As econometrics we consider a scientific and an academic discipline devoted to the development and application of statistical methods to study economic phenomena and processes, in short, statistical methods in economics. Therefore, we can say that a lot of domestic books and articles, in particular, the works by the author of this publication from the beginning of the 70s, are the parts of econometrics. However, in this article we consider only the works, in the titles of which we can see the word of "econometrics". In our country the term "econometrics" has become popular since the mid 90s. However, many publications and training courses are still developed in the western outdated paradigm. They do not conform to the new paradigm of mathematical methods of economics, the new paradigm of applied statistics and mathematical statistics, mathematical methods of research. Russian science school in the field of econometrics operates within the scientific school in the field of probability theory and mathematical statistics based by A.N. Kolmogorov. Russian science school is developed in accordance with the new paradigm of mathematical methods. It is necessary to examine the main results of Russian scientific schools in the field of econometrics. We present the information on the institutional design of national scientific schools in econometrics, in particular, on the activities of the Institute of High Technologies statistics and econometrics