The paper deals with the construction of an algorithm for nonparametric estimation of the transition probability distribution density for the Markov model of the dynamics of the number of university employees. The problem of reconstruction of transition probability distribution density on a retrospective sample of small volume is solved. The result of the solution is the transition probability matrix, the elements of which are random variables with distribution laws obtained from retrospective data. A distinctive feature of the algorithm is the simultaneous consideration of regional and functional constraints on the values of transition probabilities. The convergence of the algorithm has been experimentally confirmed.

Keywords: probability density, nonparametric estimation, Markov model, simulation modeling, university staff movement, publication activity

The article proposes a structural and functional model of the performance management system of Russian universities based on rating ratings. The system consists of four main integrated components related to each other: the Ministry of Science and Higher Education of the Russian Federation, national and international rating agencies, Russian universities and the unit for evaluating integral indicators of universities by type of activity. The particular indicators of the activity of universities that most affect the values of integral ones are determined. The key factors influencing particular indicators are identified. Such a structural and functional representation of the performance management system of universities allows us to apply statistical and simulation modeling methods for its analysis. The use of the proposed model will make it possible to predict the values of private indicators of the activity of universities, depending on the key factors of influence. This will allow the management of universities to make scientifically-based management decisions to improve the efficiency of the functioning of universities.

Keywords: performance indicators, scientific activity, publication activity, modeling, Scopus, Web of Science

This article presents the results which purpose of cases of intentional distortion of "objective" ratings based on individual indicators. The aim of the study was to build an algorithm for determining unfairly placed places in the ranking as a result of manual adjustment. The main tool is the identification of the weight coefficients of private ratings with a known form of the functional dependence of the overall rating. The analysis of techniques of creation of one of popular Russian ratings was for this purpose carried out, mathematical models of dependence of the general rating of higher education institution on its private ratings are received. It was revealed the presence of subjectivity in the construction of the rating, which manifests itself in the form of an unfair score. Standard methods do not allow to reveal such non-random "emissions" and do not provide an objective assessment. The offered algorithm allows to find such "emissions", to exclude them from selection and define fair values. The offered algorithm can be useful for heads of universities to check the correctness of the occupied place in the ranking of their organization and understanding of their real position among other universities.

Keywords: statistical analysis, multiple linear regression, ordinary least squares, approximation, rankings, indicators, higher education

Data mining methods were used to analyze publication activity of the university staff on the example of the Petrozavodsk State University (hereinafter referred to as PetrSU). In order to identify employees groups with similar indicators of scientific activity, they were clustered. As a result, teaching staff was divided into eight clusters, three of which included employees representing both present and future of science at Petrozavodsk State University, and others that would strive to get into these groups. The presented results of indicators’ statistical processing can be useful for university self-analysis. The university management could draw a conclusion on a current state of scientific activity, both of an individual employee and of the organization as a whole. This will allow to make scientifically-based management decisions in order to improve scientific performance of the organization.

Keywords: university permormance, scientific activity, data mining, clustering, scientometric indicators, h-index, RSCI

This article presents some changes in the Russian science citation index, influencing the evaluation of scientometric indicators scientists, which in turn affects the assessment of scientific activities of the organization as a whole.

Keywords: scientometric indicators, performance indicators, scientist, RSCI, strategic management, higher education