Scientific Journal of KubSAU

Polythematic online scientific journal
of Kuban State Agrarian University
ISSN 1990-4665
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Name

Orlov Alexander Ivanovich

Scholastic degree




Academic rank

professor

Honorary rank

Organization, job position

Bauman Moscow State Technical University
   

Web site url

Email

prof-orlov@mail.ru


Articles count: 123

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447 kb

ANALYSIS OF EXPERT ORDERINGS

abstract 1121508002 issue 112 pp. 21 – 51 30.10.2015 ru 712
In various applications it is necessary to analyze some expert orderings, ie clustered rankings of examination objects. These areas include technical studies, ecology, management, economics, sociology, forecasting, etc. The objects may make samples of the products, technologies, mathematical models, projects, job applicants and others. We obtain clustered rankings which can be both with the help of experts and objective way, for example, by comparing the mathematical models with experimental data using a particular quality criterion. The method described in this article was developed in connection with the problems of chemical safety and environmental security of the biosphere. We propose a new method for constructing a clustered ranking which can be average (in the sense, discussed in this work) for all clustered rankings under our consideration. Then the contradictions between the individual initial rankings are contained within clusters average (coordinated) ranking. As a result, ordered clusters reflects the general opinion of the experts, more precisely, the total that is contained simultaneously in all the original rankings. Newly built clustered ranking is often called the matching (coordinated) ranking with respect to the original clustered rankings. The clusters are enclosed objects about which some of the initial rankings are contradictory. For these objects is necessary to conduct the new studies. These studies can be formal mathematics (calculation of the Kemeny median, orderings by means of the averages and medians of ranks, etc.) or these studies require involvement of new information from the relevant application area, it may be necessary conduct additional scientific research. In this article we introduce the necessary concepts and we formulate the new algorithm of construct the coordinated ranking for some cluster rankings in general terms, and its properties are discussed
286 kb

APPLIED STATISTICS – THE STATE AND THE PROSPECTS

abstract 1191605003 issue 119 pp. 44 – 74 31.05.2016 ru 122
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
271 kb

ASYMPTOTIC METHODS OF STATISTICAL CONTROL

abstract 1021408001 issue 102 pp. 1 – 31 31.10.2014 ru 870
Statistical control is a sampling control based on the probability theory and mathematical statistics. The article presents the development of the methods of statistical control in our country. It discussed the basics of the theory of statistical control – the plans of statistical control and their operational characteristics, the risks of the supplier and the consumer, the acceptance level of defectiveness and the rejection level of defectiveness. We have obtained the asymptotic method of synthesis of control plans based on the limit average output level of defectiveness. We have also developed the asymptotic theory of single sampling plans and formulated some unsolved mathematical problems of the theory of statistical control
282 kb

ASYMPTOTICS OF ESTIMATES OF PROBABILITY DISTRIBUTION DENSITY

abstract 1311707070 issue 131 pp. 832 – 860 29.09.2017 ru 179
Nonparametric estimates of the probability distribution density in spaces of arbitrary nature are one of the main tools of non-numerical statistics. Their particular cases are considered - kernel density estimates in spaces of arbitrary nature, histogram estimations and Fix-Hodges-type estimates. The purpose of this article is the completion of a series of papers devoted to the mathematical study of the asymptotic properties of various types of nonparametric estimates of the probability distribution density in spaces of general nature. Thus, a mathematical foundation is applied to the application of such estimates in non-numerical statistics. We begin by considering the mean square error of the kernel density estimate and, in order to maximize the order of its decrease, the choice of the kernel function and the sequence of the blur indicators. The basic concepts are the circular distribution function and the circular density. The order of convergence in the general case is the same as in estimating the density of a numerical random variable, but the main conditions are imposed not on the density of a random variable, but on the circular density. Next, we consider other types of nonparametric density estimates - histogram estimates and Fix-Hodges-type estimates. Then we study nonparametric regression estimates and their application to solve discriminant analysis problems in a general nature space
279 kb

ASYMPTOTICS OF QUANTIZATION, SELECTION OF THE NUMBER OF GRADATIONS IN THE SOCIOLOGICAL QUESTIONNAIRES AND TWO-LEVEL MODEL OF INVENTORY MANAGEMENT

abstract 1231609045 issue 123 pp. 660 – 687 30.11.2016 ru 236
We consider an approach to the transition from continuous to discrete scale which was defined by means of step of quantization (i.e. interval of grouping). Applied purpose is selecting the number of gradations in sociological questionnaires. In accordance with the methodology of the general stability theory, we offer to choose a step so that the errors, generated by the quantization, were of the same order as the errors inherent in the answers of respondents. At a finite length of interval of the measured value change of the scale this step of quantization uniquely determines the number of gradations. It turns out that for many issues gated it is enough to point 3 - 6 answers gradations (hints). On the basis of the probabilistic model we have proved three theorems of quantization. They are allowed to develop recommendations on the choice of the number of gradations in sociological questionnaires. The idea of "quantization" has applications not only in sociology. We have noted, that it can be used not only to select the number of gradations. So, there are two very interesting applications of the idea of "quantization" in inventory management theory - in the two-level model and in the classical Wilson model taking into account deviations from it (shows that "quantization" can use as a way to improve stability). For the two-level inventory management model we proved three theorems. We have abandoned the assumption of Poisson demand, which is rarely carried out in practice, and we give generally fairly simple formulas for finding the optimal values of the control parameters, simultaneously correcting the mistakes of predecessors. Once again we see the interpenetration of statistical methods that have arisen to analyze data from a variety of subject areas, in this case, from sociology and logistics. We have another proof that the statistical methods - single scientificpractical area that is inappropriate to share by areas of applications
247 kb

AVERAGE VALUES AND RULES OF LARGE NUMBERS IN THE SPACES OF ARBITRARY ORIGIN

abstract 0891305038 issue 89 pp. 556 – 586 29.05.2013 ru 1320
The new results of the sample average values in different spaces and rules of large numbers for them are given in the article. We also introduced the weighted average values of type I corresponding to the sample, and type II, corresponding to the set of order statistics. The evolution of ideas about the Kemeny distance and the Kemeny median is traced. The modified Kemeny median, convenient for computation and avoiding the effect of the "center of the bagel hole" is proposed. As a generalization of the Kemeny median, we introduced and studied the empirical and theoretical values in the spaces of arbitrary origin. For them, we proved the rules of large numbers
233 kb

BASIC IDEAS OF INTERVAL DATA STATISTICS

abstract 0941310060 issue 94 pp. 868 – 893 27.12.2013 ru 1477
In the article we have considered the basic idea of asymptotic mathematical statistics of interval data, in which the elements of a sample are not the numbers, but the intervals. Algorithms and conclusions of interval data statistics fundamentally different from the classical ones. The results related to the basic concepts of notna and rational sample sizes are listed. Interval data statistics as an integral part of the system of fuzzy interval mathematics is shown
222 kb

BASIC RESULTS OF THE MATHEMATICAL THEORY OF CLASSIFICATION

abstract 1101506014 issue 110 pp. 220 – 240 30.06.2015 ru 541
The mathematical theory of classification contains a large number of approaches, models, methods, algorithms. This theory is very diverse. We distinguish three basic results in it - the best method of diagnosis (discriminant analysis), an adequate indicator of the quality of discriminant analysis algorithm, the statement about stopping after a finite number of steps iterative algorithms of cluster analysis. Namely, on the basis of Neyman - Pearson Lemma we have shown that the optimal method of diagnosis exists and can be expressed through probability densities corresponding to the classes. If the densities are unknown, one should use non-parametric estimators of training samples. Often, we use the quality indicator of diagnostic algorithm as "the probability (or share) the correct classification (diagnosis)" - the more the figure is the better algorithm is. It is shown that widespread use of this indicator is unreasonable, and we have offered the other - "predictive power", obtained by the conversion in the model of linear discriminant analysis. A stop after a finite number of steps of iterative algorithms of cluster analysis method is demonstrated by the example of k-means. In our opinion, these results are fundamental to the theory of classification and every specialist should be familiar with them for developing and applying the theory of classification
304 kb

CHARACTERIZATION OF AVERAGE VALUES BY MEANS OF MEASUREMENT SCALES

abstract 1341710070 issue 134 pp. 853 – 883 29.12.2017 ru 40
According to measurement theory, statistical data are measured in various scales. The most widely used ordinal scale, scales of intervals and relations. Statistical methods of data analysis should correspond to the scales in which the data is measured. The term "correspondence" is specified with the help of the concepts of an adequate function and an allowable scale transformation. The main content of the article is a description of the average values that can be used to analyze data measured in the ordinal scale, interval and relationship scales, and some others. The main attention is paid to the means for Cauchy and the means for Kolmogorov. In addition to the mean, from this point of view, polynomials and correlation indices are also analyzed. Detailed mathematical proofs of characterization theorems are given for the first time in scientific periodicals. It is shown that in the ordinal scale there are exactly n average values, that can be used, namely, n order statistics. The proof is represented as a chain of 9 lemmas. In the scale of intervals from all Kolmogorov means, only the arithmetic mean can be used. In the scale of relations from all the Kolmogorov means, only the power means and the geometric mean are permissible. The kind of adequate polynomials in the relationship scale is indicated
4179 kb

COGNITIVE FUNCTIONS AS A GENERALIZATION OF THE CLASSICAL CONCEPT OF FUNCTIONAL DEPENDENCE ON THE BASIS OF INFORMATION THEORY IN ASC-ANALYSIS AND SYSTEM FUZZY INTERVAL MATHEMATICS

abstract 0951401007 issue 95 pp. 122 – 183 30.01.2014 ru 1287
This article briefly reviews the classical concept of functional dependence in mathematics, determines the limitations of this concept for adequate modeling of reality and formulates the problem, consisting in search of such generalization of the concept of func-tions, which is more suitable for the adequate reflec-tion of causal relationships in the real domain. Also, it discusses theoretical and practical solving the prob-lem, consisting in: (a) we suggest the universal method of calculating the amount of information in the value of argument about the meaning of the function, i.e. cognitive functions which is independent from the subject area; b) we offer software tools: Eidos intelli-gent system, allowing in practice to carry out these calculations, i.e. to build cognitive functions based on a fragmented noisy empirical data of high dimension. We also offer the concepts of nonreducing, partially and completely reduced direct and inverse, positive and negative cognitive functions and the method of formation of reduced cognitive function, which is a generalization of known weighted least-squares meth-od on the basis of observation the amount of infor-mation in the values of the argument about the values of the functions accounting
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