Methods of Regularities Searching Based on Optimal Partitioning

The purpose of discussed optimal valid partitioning (OVP) methods is uncovering of ordinal or continuous explanatory variables effect on outcome variables of different types. The OVP approach is based on searching partitions of explanatory variables space that in the best way separate observations with different levels of outcomes. Partitions of single variables ranges or two-dimensional admissible areas for pairs of variables are searched inside corresponding families. Statistical validity associated with revealed regularities is estimated with the help of permutation test repeating search of optimal partition for each permuted dataset. Method for output regularities selection is discussed that is based on validity evaluating with the help of two types of permutation tests

About New Pattern Recognition Method for the Universal Program System “Recognition”

In this work the new pattern recognition method based on the unification of algebraic and statistical approaches is described. The main point of the method is the voting procedure upon the statistically weighted regularities, which are linear separators in two-dimensional projections of feature space. The report contains brief description of the theoretical foundations of the method, description of its software realization and the results of series of experiments proving its usefulness in practical tasks

Verification of the Returns to Scale of Production Type for the Russian Federation Regions

Monte-Carlo methods to asses a statistical validity of the relationship between coefficients of time series regression model were proposed. In economics such a relationship is present in the case when constant return to scale in production functions is assumed. The techniques being discussed here are virtually free from assumptions about underlying probability distributions and may be used in the case, when target variable or regressors are time series with random walk. This is achieved by comparing the regression model built on truly multivariate time series with those built on simulated time series with random walk. It has been shown that for the production functions of most Russian regions, the returns to scale significantly differs from a constant value at p<0.05

Data Mining in Institutional Economics Tasks

The paper discusses problems associated with the use of data mining tools to study discrepancies between countries with different types of institutional matrices by variety of potential explanatory variables: climate, economic or infrastructure indicators. An approach is presented which is based on the search of statistically valid regularities describing the dependence of the institutional type on a single variable or a pair of variables. Examples of regularities are given

Electromagnetic interferences in transistor converters and methods of interferences mitigation

This paper describes the main types of conductive electromagnetic interference that occur in modern high-frequency transistor converters and shows methods for these interferences’ mitigation. A network interference-suppression device which makes it impossible penetration of conductive electromagnetic interference (EMI) from a consumer to a mains and back is introduced. The device also provides the complete galvanic decoupling between the power mains and the consumer. This is achieved through the introduction of an intermediate link between the consumer and the network, the link being powered by a rechargeable battery, and time separation of electrical energy between consumption and transmission cycles due to the special algorithm of charging and discharging the batteries of the consumer. It provides mutual protection of the consumer and the network from all types of conductive EMI, as well as protection of the consumer from possible electric shock

Epidemiological clustering of Russian regions for the socio-economic forecast of Covid-19 rates

The paper analyzes 3 clusters that differ in the growth rate of Covid-19 from the point of view of the socio-economic structure of the regions of the Russian Federation. In addition, the database also contains clinical indicators characterizing morbidity in the regions, indicators of nosocomial infection, investment parameters and the state of the transport system. Cluster analysis methods was carried out to identify the relationship between socio-economic characteristics of regions. The first cluster is more densely populated, and the regions assigned to the second cluster are removed from each other. Perhaps for this reason, the indicators of the transport system turned out to be less important than socio-economic ones for the spread of infection. The analysis was carried out using machine learning methods based on original methods of optimally reliable partitions and statistically weighted syndromes. The results of comparing the dynamics of Covid-19 spread in clusters 1 and 3, 2 and 3 strongly indicate the importance of studying traffic flows, especially in cities with high population density. The mathematical methods used are an effective tool for the purposes of not only epidemiological analysis, but also for a systematic analysis of the functioning of the socio-economic activity of the population of interacting regions, as well as the role of transport in this process