Publications

This paper describes the results of research project on optimal pricing for LLC "Perm Local Rail Company". In this study we propose a regression tree based approach for estimation of demand function for local rail tickets considering high degree of demand heterogeneity by various trip directions and the goals of travel. Employing detailed data on ticket sales for 5 years we estimate the parameters of demand function and reveal the significant variation in price elasticity of demand. While in average the demand is elastic by price, near a quarter of trips is characterized by weakly elastic demand. Lower elasticity of demand is correlated with lower degree of competition with other transport and inflexible frequency of travel.

  In many practical tasks it is needed to estimates effect of a treatemnt on individual level. For example in medicine it is essential to determine the patients that would benifit from a certain medicament. In marketing knowning the persons that are likely to buy a new product would reduce the amount of spam. In this chapter we review the methods to estimate individulize treatment effect from a randomized trial, i.e., an experiment when a part of individuals recieves a new treatment, while the others does not. Finally, it is shown that new efficient methods are needed in this domain.

In this paper, we analyze a new approach for demand prediction in retail. One of the signicant gaps in demand prediction by machine learning methods is the unaccounted sales data censorship. Econometric approaches to modeling censored demand are used to obtain consistent and unbiased estimates of parameters. These approaches can also be transferred to different classes of machine learning models to reduce the prediction error of sales volume. In this study we build two ensemble models to predict demand with and without demand censorship, aggregating predictions for machine learning methods such as Linear regression, Ridge regression, LASSO and Random forest. Having estimated the predictive properties of both models, we test the best predictive power of the models with accounting for the censored nature of demand.

Trial-to-trial variability of the motor evoked potentials (MEP) to transcranial magnetic stimulation (TMS) is a well-known phenomenon. However, the relationship between the fluctuations of the different types of the motor output and other motor system parameters such as corticospinal excitability, interhemispheric inhibition (IHI) and their interhemispheric asymmetry have not yet been fully investigated. We studied 20 young healthy right-handed volunteers. Four TMS sessions were performed (two single-pulse TMS and two paired-coil TMS with IHI paradigm sessions for each hemisphere), 70 stimuli were delivered during every session. Coefficient of quartile variation (CQV) was used to quantify trail-to-trail variability of MEPs amplitude. Resting motor threshold values were correlated between hemispheres (r = .842, p < .001). IHI phenomenon from the left hemisphere was obtained in 18 out of 20 volunteers, while IHI phenomena from the right hemisphere was shown in 16 out of 20. A strong correlation between the variability of MEP‘s amplitudes during IHI paradigm and the degree of IHI was found for the left hand (r = −.718, p < .001). We also observed a strong correlation between CQV of MEPs from both hands to single-pulse TMS (r = .632, p = .004). A side-specific correlation between the variability of the responses to single-pulse and paired-coil TMS was found for the dominant hemisphere (r = .524, p = .021). Our preliminary results demonstrate the importance of the trial-to-trial variability of the MEPs and its interhemispheric specificity as a defining characteristics of the motor system. This study was partially supported by ofi-m RFBR grant 17-29-02518, by HSE Basic Research Program and Russian Academic Excellence Project ‘5-100’.

Russian energy system is one of the largest among centralized ones in the world. For electricity consumption planning it is essential to consider heterogeneity of energy system areas in terms of both consumption structure and climate conditions. In this paper, the relationship of electricity consumption and air temperature is investigated on the data for 64 Russian regions. Hierarchical and non-hierarchical cluster analysis is employed to form homogeneous groups of regions: three temperature clusters are retrieved. Bearing in mind different electricity needs in regions, piecewise regression with endogenous reference temperature is estimated for each temperature cluster. In all clusters both cooling and heating effects are clearly observable but reference temperature differs. For the clusters of hot and middle climate regions cooling effect prevails, while heating effect dominates in the cold regions cluster. These effects consideration in energy consumption planning may result in a higher quality of forecasting. This is of a great importance for wholesale electricity market agents and functioning of Russian energy system as a whole.

In this research we analyze the demand for performing arts. Since the observed demand is limited by the capacity of house, one needs to account for demand censorship. The presence of consumer segments with different purposes of going to the theatre and willingness-to-pay for performance and ticket characteristics compels to account for heterogeneity in theatre demand. In this paper we propose an estimator for prediction of demand that accounts for both demand censorship and preferences heterogeneity. The estimator is based on the idea of classification and regression trees and bagging prediction aggregation. We extend the algorithm for censored data prediction problem. Our algorithm predicts and combines predictions from both discrete and continuous parts of censored data. We show that the estimator is better in prediction accuracy compared with estimators which account for censorship or heterogeneity of preferences only.

Many economic applications including optimal pricing and inventory management requires prediction of demand based on sales data and estimation of sales reaction to a price change. There is a wide range of econometric approaches which are used to correct a bias in estimates of demand parameters on censored sales data. These approaches can also be applied to various classes of machine learning models to reduce the prediction error of sales volume. In this study we construct two ensemble models for demand prediction with and without accounting for demand censorship. Accounting for sales censorship is based on the idea of censored quantile regression method where the model estimation is splitted on two separate parts: a) prediction of zero sales by classification model; and b) prediction of non-zero sales by regression model. Models with and without accounting for censorship are based on the predictions aggregations of Least squares, Ridge and Lasso regressions and Random Forest model. Having estimated the predictive properties of both models, we empirically test the best predictive power of the model that takes into account the censored nature of demand. We also show that machine learning method with censorship accounting provide bias corrected estimates of demand sensitivity for price change similar to econometric models.

With an increased interest in machine processable data and with the progress of semantic technologies, many datasets are now published in the form of RDF triples for constituting the so-called Web of Data. Data can be queried using SPARQL but there are still needs for integrating, classifying and exploring the data for data analysis and knowledge discovery purposes. This research work proposes a new approach based on Formal Concept Analysis and Pattern Structures for building a pattern concept lattice from a set of RDF triples. This lattice can be used for data exploration and in particular visualized thanks to an adapted tool. The specific pattern structure introduced for RDF data allows to make a bridge with other studies on the use of structured attribute sets when building concept lattices. Our approach is experimentally validated on the classification of RDF data showing the efficiency of the underlying algorithms.

Studying the heterogeneity of consumers allows to price the product differently for consumer segments or groups of a product. In this paper we estimate a model of aggregate demand for Perm Opera and Ballet Theatre focusing on the heterogeneity in price effect on demand for tickets on different performances and seats. We estimate parameters of demand function using censored quantile regression that accounts for the limited capacity of the theatre house. We reveal the price effect variation across different types of theatrical productions and seats with lower elastic demand on ballets and for seats of higher quality.

Following publication is a conceptual article solving the problem of responses to challenges of contemporary education. Contemporary students are often characterized as incapable of holding their attention on the same object for long. Requirements of the labor market are constantly changing, requiring new competencies, skills, work styles from university alumni. Universities have to adopt to these societal changes to remain in demand. The study is aimed at review and analysis of educational approaches created to solve this problem. The approaches reviewed here have a common practice: use of agile methods of project management. Agile is a methodology of flexible project management. Its main specific features are the raise of business’s sensitivity to changes of preferences of consumers and project owners and management of communications with them, ending with perpetual improvement of the end product. Starting in computer software development, agile was quickly adopted by project management and general management on different markets. Yet there has been no such success in transition of agile to education so far. To achieve the stated aim, following methods were used: collection and analysis of scientific publications on agile in education. A part of this analysis was the comparison of conceptually important elements of these approaches to each other and the main ideas expressed in Agile Manifesto. Special attention was given to two of the most discussed approaches: agile teaching-learning methodology (ATLM) and eXtreme Pedagogy (XP). The analysis allowed for characterising the agile approaches as generally uniform and universal, i. e. they can be applied to different disciplines, despite of disciplines being aimed at theoretical or practical preparation of the students. Analysis of the values of different agile educational programs allowed for concluding that methodology can be easily adopted to solving specific educational problems in the universities practically without changing the central ideas expressed in Agile Manifesto. The originality of the data produced in the survey is based on the following characteristics: 1) the comparison of the main agile principles used in the education; 2) conceptual argumentation of possibility of using agile in university environment; 3) preparation of recommendations for the use of the agile at universities. The analysis of educational practices and approaches allowed authors to formulate following practical recommendations for creating new educational programs. First, applied educational practices should correspond to the values of adopted agile methodology. Second, the iterative interaction between the teacher and the student should become a basis of the educational practices. The result of interaction can be in the form of both student knowledge and changes in educational program.

Many of business tasks requires revealing of consumer preferences. There are two main sources of preferences: actual sales data and consumer survey data. Both of these sources have substantial drawbacks. That is the reason why they are often combined into one dataset. In this paper we propose a methodology to estimate heterogeneous preferences on a dataset combined from transactional sales database and result of discrete choice experiment. We show that collection of experimental data allows to increase an efficiency of preferences estimates and improve a model quality in terms of actual choice prediction.

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Government-subsidized theatres in Russia aim to increase both revenue and number of tickets sold. Achievement of such a goal is closely connected with study of theatre tickets demand function focusing on the estimation of price elasticity of demand. Previous papers studied price elasticity of demand for theatrical performances provide controversial results. Estimates of price elasticity varies with countries, theatres, segments of theatre audience and even with level of data aggregation. Ticket sales and price data aggregation at the national level or theatre level typically does not allow to control for the differences in produced cultural goods quality. Incorrect control or ignoring of a theatre, performances and seats quality leads to a problem of omitted variables and bias in price elasticity estimates. In this paper we employ disaggregated ticket sales data for four seasons of Perm opera and ballet theatre and estimate the price effect on the theatrical demand for various performance types and seats in the hall. We use censored quantile regression to estimate the parameters of theatre demand function. We reveal the weak elasticity of demand for Perm opera and ballet theatre. We also show that ignoring the limited capacity of the theatre hall, characteristics of performances and seats quality leads to biased estimates of demand function parameters.

Russian book market is one of the largest in the world in terms of new titles in print. However, this market is  underexplored. There is no research dealing with an empirical demand or supply function estimation for this market. The purpose of this paper is to analyze the book demand function and to check whether this kind of demand is price and income elastic. On the basis of results retrieved, managerial recommendations are to be offered. For this purpose, the demand function for books is built and estimated both on the total sample and for particular literature genres. The peculiarity of the demand model estimation is the introduction in the model covariates indicating the book content quality such as the amount of people who gave rating on the website, average rating of the book from the website and the combined effect of these two variables. An empirical estimation of these factors influence has not been considered in the previous research yet. Model estimation was based on the data of one North-Western Federal District book retail chain. According to the estimation results, book demand is price inelastic; moreover, books are estimated to be luxury goods. The analysis of demand functions for separate genres suggests that the demand for each genre is price-insensitive. Only Russian and foreign prose, foreign fiction and poetry are luxury goods among all the genres analyzed. A foreign detective, Russian fiction and sentimental novel are normal goods, whereas Russian detective is an inferior goods. The results of the research might be of a particular interest for books retail chains and publishers

Recovery after stroke relates tightly to the white matter integrity. Currently, the main methodology for non-invasive white matter integrity assessment is diffusion-weighted magnetic resonance imaging (DW-MRI), a state-of-the-art approach which is, however, prone to multiple limitations. Using DW-MRI, it was demonstrated that many pathways including corticospinal tract (CST) and corpus callosum contribute to structural brain reserve after stroke, but only a few of these tracts were found to be useful in the clinical practice. The most widely known measure is an asymmetry of the fractional anisotropy (FA) in CST at the level of the internal capsule, which could be used for predicting motor recovery in acute stroke. Recently, a new complementary motor component of the structural reserve, the so-called alternate motor fibers (AMFs), was proposed for motor recovery prognosis in stroke patients, and it was even reported to correlate with the effect of the transcranial direct current stimulation in chronic stroke. Here, we would like to point out a possible additional sensory interpretation of the AMF that appears plausible after taking into account technical limitations of DW-MRI approach, which may potentially give rise to different interpretations of the same results.

In the times of Soviet Union books were a luxury good. This paper examines whether books are still a luxury good in Russia. For this purpose data from one of the Russian book retail chains is used to empirically estimate a general book demand and separate demand models for genres. We focus on estimating income elasticity. For this reason we construct a covariate on the basis of monthly wages of working individuals that reveals consumer income. Moreover, this paper is one of the few which addresses in detail the influence of books content quality on book demand. The main result is that books on average are not luxury goods anymore in Russia. However two genres: foreign prose and poetry are exceptions and can be called luxury goods. We also conclude that quality control covariates (book rating and number of people who rated the book) are important determinants of book demand as they influence significantly the general book demand and the demand models for different genres as well.

In this study, we use a sample of 192 listed shipping companies and employ a logit model in order to investigate the determinants of the probability of default. We enhance our analysis by isolating not only the cases of company liquidations but also those cases where companies had to change their legal status due to warning liquidity signals. Our key findings are in line with prior research and moreover we depict a changing trend in the marginal effects of relevant variables, on the probability of default. We further show, through an empirical application, how the obtained results can be used in a managerial decision-making process and in a bank credit underwriting process in order to assess the creditworthiness of a shipping company.

A scalable method for mining graph patterns stable under subsampling is proposed. The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far. We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns. The experiments on numerous graph datasets show that gSOFIA is very efficient for discovering subsample-stable graph patterns.  

People are intent to make similar choices especially in consumer goods markets. To address both explanations of this persistence, i.e. state dependence and heterogeneity in preferences, we use random coefficient logit model based on scanner panel data on juice purchases. The product differentiation of the chosen category allows us to model three dimensions of state dependence on brand, size and flavor characteristics. We provide evidence that the persistence in brand choices is positively correlated with persistence in size and flavor choices, thus the consumer pattern is prone to be inertial or variety seeking in every product characteristics. Simultaneously we show that the more sensitive to price and promotional activities consumers are, the less inertial is their behavior.