Scientific Journal of KubSAU

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

Statistical methods are widely used in domestic feasibility studies. However, for most managers, economists and engineers, they are exotic. This is because modern statistical methods are not taught in the universities. We discuss the situation, focusing on the statistical methods for economic and feasibility studies, ie, econometrics. In the world of science, econometrics has a rightful place. There are scientific journals in econometrics, Nobel Prizes in Economics are awarded to series of researches in econometrics. The situation in the field of scientific and practical work and especially the teaching of econometrics in Russia is disadvantaged. Often, individual particular constructions replace econometrics in general, such as those related to regression analysis. In econometrics we select three types of scientific and applied activities: development and study of methods of applied statistics, taking into account the specifics of economic data; development and study of econometric models, in accordance with the specific needs of economic science and practice; the use of econometric methods for statistical analysis of specific economic data. This article describes these three types of scientific and applied activities. We discuss the specificity of economic data. We show the importance of economic non-numeric values. We discuss the statistics of interval data - scientific direction at the joint of metrology and statistics. We give the representation of the econometric models. Problems of application of econometric methods are considered as an example of inflation. We discuss the statistics and econometrics as the field of scientific and practical activities. We have examined econometric methods in practical and training activities

The founder of the economic theory is Aristotle. The so-called "market economy" is a perversion of Aristotle's views. We have to eliminate distortions. What can replace the "market economy"? We are developing a new organizational-economic theory - solidary information economy, based on the views of Aristotle. The name of this theory has changed over time. Initially, we used the term "nonformal information economy of the future", and then began to use the term "solidary information economy." In connection with Biocosmology and neo-Aristotelism preferred is an adequate term "functionalist organic information economy". This article describes the main provisions of solidary information economy, intended to replace the market economy as a management tool. The main problems are discussed, the solution of which is devoted to research related to the considered basic organizational and economic theory. We discuss Aristotle's positions, on which the economic theory is based, in particular, solidary information economy. We prove that the market economy has remained in the XIX century and the mainstream in modern economic science - justification of insolvency of a market economy and the need to move to a planned system of economic management. We examine the impact of ICT on economic activity. We develop the approaches to decision-making in the solidary information economy. On the basis of modern decision theory (especially expert procedures) and informationcommunication technologies people can get rid of chrematistics and will understand the term of "economy" according to Aristotle

In accordance with the new paradigm of mathematical statistics the statistics of objects of nonnumerical nature (statistics of nonnumerical objects, non-numerical data statistics, non-numeric statistics) is one of the four main areas of mathematical statistics. Statistics of objects of nonnumerical nature consists of a central core - statistics in spaces of arbitrary nature - and statistical theories of analysis of specific types of non-numeric data. To identify possibilities of application of statistics of objects of nonnumerical nature it is useful to explore the multiformity of objects of non-numeric nature. This is the subject of this article. We have considered the results of measurements at scales other than absolute; binary relations; dichotomous (binary) data; sets. We have also analyzed the objects of non-numerical nature as statistical data, and their importance in the formation of statistical or mathematical model of a real phenomenon, as a result of data analysis

We introduce the concept of "controlling organizational-economic methods". We define the terms in the sequence "the problem - the model - the method - the conditions of applicability". We have described the basic organizational-economic model of industrial firm; by means of this model we have discussed the problems of development of modern organizational-economic methods. We have demonstrated the relevance of the theory and methodology of organizational-economic modeling. For example, we consider the application of statistical methods at various stages of the life cycle of the product, the problem of internal risks in an industrial firm and accounting for inflation in the analysis of activities of the organization

The article is devoted to the methods of analysis of statistical and expert data in problems of economics and management that are discussed in the framework of scientific specialization "Mathematical methods of economy", including organizational-economic and economic-mathematical modeling, econometrics and statistics, as well as economic aspects of decision theory, systems analysis, cybernetics, operations research. The main provisions of the new paradigm of this scientific and practical field are developed by the author of this article in the 1980s during the creation of the All-Union Statistical Association. The new paradigm is compared with the old (corresponding to the middle of XX century). Is summarized monographs, textbooks and manuals prepared under the leadership of the author of this paper in the XXI century according to the new paradigm

In 1979, non-numerical data statistics was singled out as an independent area of applied statistics. Initially, the term "statistics of objects of non-numerical nature" was used to denote this area of mathematical methods of economics. Our basic non-numeric statistics textbook is called "Non-Numeric Statistics". Non-numerical data statistics is one of the four main areas of applied statistics (along with number statistics, multidimensional statistical analysis, statistics of time series and random processes). Statistics of non-numerical data are divided into statistics in spaces of a general nature and sections devoted to specific types of non-numerical data (statistics of interval data, statistics of fuzzy sets, statistics of binary relations, etc.). Currently, statistics in spaces of a general nature is the central part of applied statistics, and non-numeric data statistics including it is the main area of applied statistics. This statement is confirmed, in particular, by the analysis of publications in the section "Mathematical Research Methods" of the journal "Industrial Laboratory. Diagnostics of Materials" - the main place of publication of russian studies on applied statistics. This article is devoted to the analysis of the basic ideas of non-numerical data statistics against the background of the development of applied statistics from the perspective of a new paradigm of mathematical research methods. Various types of non-numeric data are described. The historical path of statistical science is analyzed. We have discussed the development of statistics of non-numerical data. The article analyzes basic ideas of statistics in spaces of a general nature: average values, laws of large numbers, extreme statistical problems, nonparametric estimates of the probability density, classification methods (diagnostics and cluster analysis), statistics of the integral type. Some statistical methods for analyzing data lying in specific spaces of non-numeric nature are briefly considered: non-parametric statistics (real distributions usually differ significantly from normal), statistics of fuzzy sets, theory of expert estimates (the Kemeny median is a sample average of expert orderings), etc. We have also discussed some unsolved problems in statistics of nonnumeric data

In many applications, we study the time series (or a random process), which is the sum of the periodic deterministic function of time and random errors that distort the periodic signal. It is required to estimate the length of the period and the periodic component. It does not assume that the periodic function is included in any parameter family of functions, such as finite sums of sines and cosines. It is obvious that the assumption of occurrence of a periodic function in parametric family does not meet the characteristics of the real world, ie, is conditional, internal mathematical (look for the keys under the lamp because there is a light, not in the bush, where lost, because there are dark). For similar reasons, it is impossible to assume that the distribution function of the random errors is included in any parameter family of distributions. In accordance with the new paradigm of mathematical statistics in this article we studied the problem of nonparametric estimation (minimum) length of the period and the periodic component of the signal. On the basis of natural variation and scope of indicators is suggested a new class of nonparametric estimators of the length of the period and the periodic component in the time series. Based on the general results of statistics of objects of non-numeric nature we proved the consistency of these estimates. From the practical point of view it is necessary to minimize the numerical (one parameter - ability length of period of time) one or more of the 66 functionals, described in the article

We continue the series of articles about the history of statistics. We discuss the development of nonparametric and applied statistics in our country in 1930 - 1980 years. We have presented the studies of the great statisticians of the twentieth century, such as N.V. Smirnov, L.N. Bolshev, V.V. Nalimov. American statistics show Russian debate about applied statistics. We have briefly listed the process of creation of the All-Union Statistical Association (1990) and its further developments

The article is devoted to the nonparametric point and interval estimation of the characteristics of the probabilistic distribution (the expectation, median, variance, standard deviation, variation coefficient) of the sample results. Sample values are regarded as the implementation of independent and identically distributed random variables with an arbitrary distribution function having the desired number of moments. Nonparametric analysis procedures are compared with the parametric procedures, based on the assumption that the sample values have a normal distribution. Point estimators are constructed in the obvious way - using sample analogs of the theoretical characteristics. Interval estimators are based on asymptotic normality of sample moments and functions from them. Nonparametric asymptotic confidence intervals are obtained through the use of special output technology of the asymptotic relations of Applied Statistics. In the first step this technology uses the multidimensional central limit theorem, applied to the sums of vectors whose coordinates are the degrees of initial random variables. The second step is the conversion limit multivariate normal vector to obtain the interest of researcher vector. At the same considerations we have used linearization and discarded infinitesimal quantities. The third step - a rigorous justification of the results on the asymptotic standard for mathematical and statistical reasoning level. It is usually necessary to use the necessary and sufficient conditions for the inheritance of convergence. This article contains 10 numerical examples. Initial data - information about an operating time of 50 cutting tools to the limit state. Using the methods developed on the assumption of normal distribution, it can lead to noticeably distorted conclusions in a situation where the normality hypothesis failed. Practical recommendations are: for the analysis of real data we should use nonparametric confidence limits