name
Orlov Alexander Ivanovich
Scholastic degree
•
•
•
Academic rank
professor
Honorary rank
—
Organization, job position
• Bauman Moscow State Technical University
Research interests
статистические методы, организационноэкономическое моделирование. Разработал новую область прикладной статистики — статистику объектов нечисловой природы
Web site url
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Current rating (overall rating of articles)
0
TOP5 coauthors
Articles count: 150
Сформировать список работ, опубликованных в Научном журнале КубГАУ

BASIC IDEAS OF INTERVAL DATA STATISTICS
01.00.00 Physicalmathematical sciences
DescriptionIn 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

MAIN PROBLEMS OF CONTROLLING OF THE QUALITY
DescriptionControlling of statistical methods to ensure product quality is the special case of controlling organizational and economic methods of management. Today, controlling in the practice of management of Russian companies is understood as "the system of informationanalytical and methodological support to achieve their goals." The controller is developing a decisionmaking rules, the head takes decisions on the basis of these rules. We proved the concept of "controlling of methods". Innovation in management is based, in particular, on the use of new adequate organizationaleconomic (as well as economicmathematical and statistical) methods. Controlling in this area  is the development and application procedures of compliance management used and newly developed (implemented) organizationaleconomic methods for the task. Thus, the methodology for controlling is of great practical value in any field in which the actions (operations) must be carried out in accordance with certain rules (regulations, standards, guidelines), as in any such area in which we need to use development and application procedures of compliance management used and the newly established (implemented) rules for solution of tasks assigned to the organization. In this article, we select a area of controlling as controlling quality, and we discuss its main issues. This is about controlling of organizationaleconomic methods to ensure product quality, especially about the statistical methods based on probability theory and mathematical statistics. We consider the analysis and synthesis of plans of statistical quality control, optimization options plans of statistical control, truncated plans. Are discussed the differences control plans provider and the consumer, the allocation of units formless (liquid, gas) products, the selection of a random sample of the statistical quality control of products, lower estimate of the required sample size. It is established, that is not always necessary quality control. Is given the theory of the basic paradox of statistical quality control. We discuss the development of statistical methods for quality control in our country. Is given the classification of statistical methods of quality management

BASIC REQUIREMENTS FOR DATA ANALYSIS METHODS (ON THE EXAMPLE OF CLASSIFICATION TASKS)
08.00.13 Mathematical and instrumental methods of Economics
DescriptionThere is a need to clean up the classification methods. This will increase their role in solving applied problems, in particular, in the diagnosis of materials. For this, first of all, it is necessary to develop requirements that classification methods must satisfy. The initial formulation of such requirements is the main content of this work. Mathematical classification methods are considered as part of the applied statistics methods. The natural requirements to the considered methods of data analysis and the presentation of calculation results arising from the achievements and ideas accumulated by the national probabilistic and statistical scientific school are discussed. Concrete recommendations are given on a number of issues, as well as criticism of individual errors. In particular, data analysis methods must be invariant with respect to the permissible transformations of the scales in which the data are measured, i.e. methods should be adequate in the sense of measurement theory. The basis of a specific statistical method of data analysis is always one or another probabilistic model. It should be clearly described, its premises justified  either from theoretical considerations, or experimentally. Data processing methods intended for use in realworld problems should be investigated for stability with respect to the tolerances of the initial data and model premises. The accuracy of the solutions given by the method used should be indicated. When publishing the results of statistical analysis of real data, it is necessary to indicate their accuracy (confidence intervals). As an estimate of the predictive power of the classification algorithm, it is recommended to use predictive power instead of the proportion of correct forecasts. Mathematical research methods are divided into "exploratory analysis" and "evidencebased statistics." Specific requirements for data processing methods arise in connection with their "docking" during sequential execution. The article discusses limits of applicability of probabilisticstatistical methods. Concrete statements of classification problems and typical errors when applying various methods for solving them are also considered

MAIN FEATURES OF THE NEW PARADIGM OF MATHEMATICAL STATISTICS
01.00.00 Physicalmathematical sciences
DescriptionThe new paradigm of mathematical statistics is based on the transition from parametric to nonparametric statistical methods, the numerical data  to nonnumeric, on the intensive use of information technology. Its distinctive features are revealed in comparison with the old paradigm of mathematical statistics in the midtwentieth century

KEY STAGES OF STATISTICAL METHODS DEVELOPMENT
01.00.00 Physicalmathematical sciences
DescriptionThe first statistical publication – the Fourth Book of Moses, “Numbers” in the Old Testament. We trace the development of ideas about the statistics until the twentieth century. The present stage of statistical methods began with parametric statistics by Pearson, Student, Fisher. Scientometrics of statistical researches provides an indication of the accumulated results. Nonparametric statistics appeared in the 1930s, applied statistics in our country  at the turn of 197080. We have discussed what gives applied statistics to national economy. Also we have told briefly about the history of statistical methods in Russia (until Kolmogorov's time)

RUSSIAN SCIENTIFIC SCHOOL IN THE ECONOMETRICS FIELD
01.00.00 Physicalmathematical sciences
DescriptionWe 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

01.00.00 Physicalmathematical sciences
DescriptionAccording to the new paradigm of applied mathematical statistics one should prefer nonparametric methods and models. However, in applied statistics we currently use a variety of parametric models. The term "parametric" means that the probabilisticstatistical model is fully described by a finitedimensional vector of fixed dimension, and this dimension does not depend on the size of the sample. In parametric statistics the estimation problem is to estimate the unknown value (for statistician) of parameter by means of the best (in some sense) method. In the statistical problems of standardization and quality control we use a threeparameter family of gamma distributions. In this article, it is considered as an example of the parametric distribution family. We compare the methods for estimating the parameters. The method of moments is universal. However, the estimates obtained with the help of method of moments have optimal properties only in rare cases. Maximum likelihood estimation (MLE) belongs to the class of the best asymptotically normal estimates. In most cases, analytical solutions do not exist; therefore, to find MLE it is necessary to apply numerical methods. However, the use of numerical methods creates numerous problems. Convergence of iterative algorithms requires justification. In a number of examples of the analysis of real data, the likelihood function has many local maxima, and because of that natural iterative procedures do not converge. We suggest the use of onestep estimates (OSestimates). They have equally good asymptotic properties as the maximum likelihood estimators, under the same conditions of regularity that MLE. Onestep estimates are written in the form of explicit formulas. In this article it is proved that the onestep estimates are the best asymptotically normal estimates (under natural conditions). We have found OSestimates for the gamma distribution and given the results of calculations using data on operating time to limit state for incisors

ESTIMATION OF INFLATION ON THE BASIS OF INDEPENDENT INFORMATION
DescriptionThis article is devoted to the investigations of our research team built for independent collection and examination the information about prices, ie to study the real inflation. The approach to measuring the rise in prices is based on selecting and fixing tool of economists and managers  the consumer basket which does not change during the time. On the basis of physiological consumption norms of the Institute of Nutrition (Russian Academy of medical Sciences) we made up the minimum consumer basket, ie we set annual consumption on food staples required to maintain normal functioning of the human body. In 19932015 we carried out an independent price collection. We obtained values of the consumer basket and inflation indices. We give the comparison with the data of official statistics. Our work is aimed at the elimination of Rosstat's monopoly in calculating the index of inflation, the minimum subsistence level and the real disposable income of the population. Using the same consumer basket makes it possible to compare the results of calculations for different time periods. That is why our works compare favorably to the approach of the official statistics. We have given a more detailed analysis of inflation in the XXI century. We have also briefly reviewed the use of inflation indices in the analysis of problems of households, organizations and production firms, as well as the country as a whole

Description
Estimates of the errors of the characteristics of financial flows of investment projects are needed to make adequate management decisions, particularly in the rocket and the space industry. Organizationaleconomic approaches to the estimations of the feasibility of innovationinvestment projects to create rocket and space technologies require intensive use of numerical characteristics of the financial flows of longterm projects of this type. In organizationaleconomic support for control problems in the aerospace industry we must provide the need to obtain the estimates of the errors of the characteristics of financial flows. Such estimates are an integral part of the organizationaleconomic support of innovation activity in the aerospace industry. They can be compared with the predictions interval, i.e. confidence estimation of predictive values. Half the length of the confidence interval is the prediction error estimate. In this article we give the new method for estimating the errors of the main characteristics of the investment projects. We focus on the net present value called NPV. Our method of estimation of errors is based on the results of statistics interval data, which is an integral part of the system fuzzy interval mathematics. We construct asymptotic theory which corresponds to small deviations of discount coefficients. The error of NPV has been found as the asymptotic notna. With up to infinitesimals of higher orders the error of NPV is a linear function of the maximum possible error of discount coefficients

ESTIMATES OF PROBABILITY DENSITY FUNCTION IN SPACES OF ARBITRARY NATURE
01.00.00 Physicalmathematical sciences
DescriptionLinear estimators of the probability of density in the spaces of an arbitrary nature and particular cases – nuclear, histogram, the FixHodges type estimates are introduced. Consistency and asymptotic normality of linear estimates are proved under natural conditions. It is shown that the probability of the area can be found by linear density estimates. A special case of a finite set are discussed, it was found that sample mode converges to the theoretical one