Multi-factor Regression Models Research Articles

ПОВЫШЕНИЕ ТОЧНОСТИ РЕГРЕССИОННЫХ МОДЕЛЕЙ МЕТОДАМИ ПОЛИНОМИАЛЬНОЙ АППРОКСИМАЦИИ

The study objective is to assess the influence of the approximation method on the accuracy of the regression model.
 The task is to increase the accuracy of regression models using polynomial approximation methods.
 Research methods: the least square method, the polynomial approximation method.
 The novelty of the work: dependencies for the regression analysis of a multifactor experiment using polynomial approximation is developed, the influence of the approximation method on the accuracy of the regression model is analyzed.
 The study results: as a result of applying the developed dependencies, the accuracy of single-factor regression equations increased by 2.4 times, two-factor equations - by 1.7 times.
 Conclusions: the use of polynomial approximation makes it possible to increase the accuracy of single-factor and multifactor regression models, which increases their adequacy, the significance of coefficients and expands the possibilities of their application.

Competitiveness of modes of transport in Kazakhstan: influencing factors and rating

This article discusses the competitiveness of various modes of transport in Kazakhstan and provides an assessment of the main factors influencing the choice of transportation modes within the realm of economics. The comprehensive rating system aimed at evaluating the competitiveness of different transportation modes in Kazakhstan. Drawing upon various perspectives and analytical methodologies, this system proves to be a valuable tool for politicians and stakeholders seeking to optimize Kazakhstan transportation system. The primary objective of this study is to examine which of the selected factors has the most significant impact on Kazakhstan's decisions regarding the choice of transportation vehicles. The thorough analysis of key literature sources was carried out and a methodology based on our findings was selected. The methodological approach involves the creation of multi-factor regression models utilizing the EViews10 data analysis software package. The research dataset encompasses data from the Bureau of National Statistics of the Republic of Kazakhstan's Agency for Strategic Planning and Reforms for the years 2013 to 2022. Calculations derived from our research revealed that the selected factors have varying effects on different types of transportation. As a result of this study, it becomes possible to formulate recommendations for state policies in the realm of transportation infrastructure and its implications for economic and social development, with a specific focus on enhancing vehicle capacity. The study's findings are of relevance to transportation companies and governmental entities responsible for managing the transportation system.

The use of Multifactor Regression Models in Forecasting in the Agricultural Sector of Yakutia

Under the constantly changing economic conditions of management, ensuring food security in the country, as well as in the regions becomes especially relevant. Agriculture is the only industry that is able to bring countries into a state of complete self-sufficiency, which is clearly indicated in the Doctrine of Food Security of Russia. With this in mind, the intensification of agricultural sectors, through the introduction of new technologies and improving the quality of land use is a particularly topical challenge. In this regard, the state faces the issues of the development of the industry and scientific forecast of its main indicators, taking into account the existing factors and conditions. This article provides a detailed methodology for developing a forecast for the agricultural sector using multivariate regression models. The main factors influencing the indicators of agriculture are considered, the main ones are highlighted and a multifactorial regression model is developed to forecast the indicator of gross agricultural output for the next five years. Recommendations are given for improving the application of this method in relation to the region under consideration.

Innovative Clusters of Global Trade Leadership

The formation of a new global system and systemic global interdependence has generated new competitiveness factors for market participants, determining their appropriate strategic behavior to ensure a highly competitive position and leadership. Therefore, the purpose of the study is to identify the countries of intellectual leaders in the global market and the factors that influence the positions that countries achieve in terms of leadership. The following research methods were used: multifactor regression models, cluster analysis, and comparative analysis. Based on the authors’ methodology for assessing countries’ intellectual leadership, the clustering of countries in the global economy is determined. The evaluation algorithm was based on three levels: 1) resources, 2) the intermediate results of intellectual activity, and 3) the final results of overall progress.
 Using a multifactor regression model and cluster analysis, four clusters of countries were identified according to key indicators of intellectual leadership. For each cluster, the specialization of the two countries in terms of merchandise exports was analyzed: cluster 1 – the United States and Germany; cluster 2 – Israel and Italy; cluster 3 – Brazil and Ukraine; cluster 4 – China and South Korea. Each country is assigned an index of economic complexity, and the change in position of each country within a cluster over ten years is noted.
 An important goal is to understand the determinants of the leadership of countries in each geographic region.
 The analysis is based on the cluster analysis carried out in previous publications. The clustering of countries was carried out based on the dynamics of macroeconomic indicators over the past 15 years.

Performance and Performance Persistence of Europe-Focused ETFs in the United States

This study examines the performance and performance persistence of US-listed exchange-traded funds (ETFs) that are focused on the European capital markets. Performance is estimated in several ways—raw returns, alphas from single-factor and multifactor regression models, and risk-adjusted returns. The sample includes 43 ETFs, and the study period spans from January 1, 2015–December 31, 2022. The results reveal that, on average, the examined ETFs cannot beat the S&P 500 Index and STOXX Europe 600 Index, which serve as proxies for the US and European stock markets, respectively. In addition, the results show that the performance of the Europe-focused ETFs does not persist.

Модель прогнозування витрат на інноваційну діяльність у промисловому секторі України

The article considers the problem of increasing the reliability of estimation of the cost of innovation activity in the industrial sector of Ukraine. Using as primary data the information of the previous study, the dependence of the costs of innovation activity on a group of factors is analyzed, as a result of which it was decided to build several multi-factor regression models. In order to build this group of models, the least squares method was used. In the course of checking the obtained multivariate models, it was found that each model has internal parameters in which the value of p-values exceeds the limit value, which means that the obtained values of the internal parameters of the model are not significant. Therefore, all multivariate models constructed using the least squares method were eliminated from further research. At the next stage of the research, one-factor regression models were constructed using the least squares method, where the amount of mastered production of new types of products (technological processes) was used as a factor. After sorting out the non-essential models, the remaining ones were compared with respect to their quality characteristics. However, in all univariate models, it was found that the calculated values of the average approximation error exceeded 10%. Therefore, yet again, all models have been eliminated from further research. Due to the impossibility of obtaining a model using the least squares method in the simulation process, it was decided to use machine learning methods with a teacher. Among the methods of machine learning, it was decided to pay attention to the following methods: k-near neighbors, regression trees, and neural network method. Taking into account that these models are not adversely affected by the multi-collinearity between factors, both the data for the models were used raw data without additional transformations. According to the results of the research, it was found that among the models using machine learning methods, the best models were the binary regression decision tree (with a hyperparameter value of max_depth = 3) and the neural network model. When comparing these models, it was found that the model based on the decision tree has a smaller value of the average approximation error, which means that this model can be recommended for use in making management decisions on forecasting the costs of innovation activity of industrial enterprises in the future.

A cohort study using IL-6/Stat3 activity and PD-1/PD-L1 expression to predict five-year survival for patients after gastric cancer resection.

The expression/activation of IL-6, p-Stat3, PD-1 and PD-L1 in gastric cancer (GC) tissues were examined to evaluate their abilities in predicting the survival prognosis in postoperative patients with GC. The clinicopathological data and paraffin-embedded tissues of 205 patients who underwent gastric cancer resection were collected at the First Affiliated Hospital of Shihezi University School of Medicine, and the patients were followed-up annually after surgery. Immunohistochemistry (IHC) was used to detect the expression of IL-6, p-Stat3, PD-1 and PD-L1 proteins using tissue microarrays derived from these patients. Statistical analyses were performed using non-parametric tests, Spearman's correlation, ROC curves, Kaplan-Meier survival analysis, Cox single-factor and multifactor regression models. In comparison, the analyses were also performed for GC patients from public databases (407 patients from TCGA and 433 patients from GEO, respectively). (1) The expression levels of IL-6, p-Stat3, PD-1 and PD-L1 in GC tissues were significantly higher than adjacent normal tissues (ANT) (81.01% vs. 52.78%, P<0.001; 100% vs. 93.41%, P<0.001; 58.58% vs. 40.12%, P<0.001; 38.20% vs. 26.90%, P = 0.025, respectively). The mean optical density (MOD) values of IL-6, p-Stat3, PD-1 and PD-L1 were significantly higher in GC tissues. (2) The higher the levels of IL-6 (P<0.001), p-Stat3 (P<0.001), and PD-L1 (P = 0.003) were, the worse the survival prognoses were observed, respectively, among GC patients. The expression of PD-1 was not correlated with the prognosis of GC patients (P>0.05). The lower the degree of cell differentiation (P<0.001) was, the worse the survival prognoses were observed among GC patients. (3) Independent risk factors for postoperative prognosis in GC patients included age (≥60 years old), poor cell differentiation, invasion depth (T3/T4), lymph node metastasis (N1-3), distant metastasis (M1), and high levels of IL-6 (2+/3+). (4) A multi-factor combination (cell differentiation+IL-6+p-Stat3+PD-1+PD-L1) appeared to be the best survival predictor for GC patients as indicated by AUC (AUC 0.782, 95% CI = 0.709, 0.856, P<0.001). This combination may be the optimal predictor for postoperative survival of GC patients. (5) The levels of IL-6, p-Stat3, PD-1 and PD-L1 correlated with the infiltration levels of various tumor-infiltrating immune cells. (6) The analyses of ROC curves, calibration, DCA and Kaplan-Meier (KM) survival curves in TCGA dataset confirmed that the nomogram model could accurately predict the prognosis in GC patients. (1) The expressed levels of IL-6, p-Stat3, PD-1 and PD-L1 are higher in GC tissues than in adjacent normal tissues. (2) The high levels of IL-6, p-Stat3 and PD-L1 are correlated with poor survival in GC patients. (3) The high levels of IL-6, p-Stat3, PD-1 and PD-L1 have influences in GC tumor microenvironment. (4) The multi-predictor combination of "IL-6+p-Stat3+PD-1+cell differentiation" serves as an optimal survival predictor for postoperative GC patients and better than the TNM staging system. As these molecules can be examined in preoperative biopsies, these observations may provide a useful guide for clinicians to strategize individualized surgical plans for GC patients before surgery.

Bayesian and maximin optimal designs for heteroscedastic multi-factor regression models

Bayesian and maximin optimal designs for heteroscedastic multi-factor regression models

Medial Temporal Atrophy Contributes to Cognitive Impairment in Cerebral Small Vessel Disease.

BackgroundThe role of brain atrophy in cognitive decline related to cerebral small vessel disease (CSVD) remains unclear. This study used AccuBrain™ to identify major CSVD-related brain changes and verified the relationship between brain atrophy and different cognition domains in CSVD patients.MethodsAll enrolled 242 CSVD patients and 76 healthy participants underwent magnetic resonance imaging examinations and detailed neuropsychological scale assessments were collected at the same time. The AccuBrain™ technology was applied to fully automated image segmentation, measurement, and calculation of the acquired imaging results to obtain the volumes of different brain partitions and the volume of WMH for quantitative analysis. Correlation analyses were used to estimate the relationship between MRI features and different cognitive domains. Multifactor linear regression models were performed to analyze independent predictors of MTA and cognitive decline.ResultsCSVD patients exhibited multiple gray matter nucleus volume decreases in the basal ganglia regions and brain lobes, including the temporal lobe (P = 0.019), especially in the medial temporal lobe (p < 0.001), parietal lobe (p = 0.013), and cingulate lobe (p = 0.036) compare to HC. The volume of PWMH was an independent predictor of MTA for CSVD patients. Both medial temporal atrophy (MTA) and PWMH were associated with cognition impairment in CSVD-CI patients. MTA mediated the effect of PWMH on executive function in CSVD-CI patients.ConclusionsOur results showed that MTA was related to cognition impairment in CSVD patients, which might become a potential imaging marker for CSVD-CI.

The influence of spinopelvic relationships on late dislocation of the prosthetic femoral head after total hip arthroplasty

Objective. To analyze the influence of sagittal balance parameters on the risk of dislocations of the head of the femoral component of the hip joint endoprosthesis.Material and Methods. A retrospective analysis of medical records of 113 patients with idiopathic coxarthrosis who underwent unilateral total hip arthroplasty was performed. The study assessed the parameters characterizing the sagittal balance in patients without prosthetic femoral head dislocation in the postoperative period (Group 1; n = 60) and in patients treated for prosthetic femoral head dislocation (Group 2; n = 53). Comparison of indicators was carried out by non-parametric Mann – Whitney U-test, and identification of dislocation predictors – by building single- and multi-factor logistic regression models. Differences were considered statistically significant at the achieved significance level p &lt; 0.05.Results. In Group 1, the type 3 sagittal balance according to Roussouly prevailed (48 %), in Group 2 – types 1, 2 and 4 (75 %). In patients with types 1 and 2 sagittal balance, the dislocations of the prosthetic femoral head occured 1.84 times more often than in patients with type 3, and that in patients with type 4 – 1.66 times more often.Conclusion. Patients with Roussouly type 3 sagittal balance have significantly lower risks of postoperative dislocation of the prosthetic femoral head, as compared with those with types 1, 2 and 4.

Regression analysis to estimate the response of the respiratory organs to exposure of air microtoxicants in chronic obstructive pulmonary disease

Inroduction. The assessment of the respiratory system is of particular importance in the context of increasing impact of anthropogenic factors on human health. Aim. Evaluation the response of the lung function indices to the effects of micro-suspensions and toxic metals in patients with chronic obstructive pulmonary disease (COPD), using regression models. Materials and methods. The study involved 60 patients with stable mild-to-moderate COPD and 30 healthy people (the control group). The analysis of lung function was carried out by spirometry and body plethysmography. Arterial oxygen saturation (SaO2 ) was measured by pulse oximetry. Exhaled CO2 concentration and blood carboxyhemoglobin (COHb%) concentration were assessed using a portable analyzer MicroCO Meter. The pollution of the city atmosphere was assessed by aerosol suspensions of solid particles in the areas of residence of the examined patients. Fractional composition of suspended particles (0-1, 1-10, 10-50, 50-100, 100-400, 400-700,&gt; 700 μm; %) were analyzed in the areas of residence of the examined patients. Toxic metal (Pb, Cr, Mn, Fe, Co, Ni, Cu, Zn; μg/L) levels in the air samples were determined. The statistical module "Multiple regression" was used for assessing the response of functional parameters of the respiratory system. Results. The analysis of one-factor and multi-factor regression models made it possible to identify both trigger factors of dust and technogenic air pollution and the synergistic effect of the influence of dominant factors. It has been shown that multifactorial reactions to technogenic impact manifest as altered blood oxygen saturation and carboxyhemoglobin level and thereby contribute to the development of respiratory failure. Conclusion. Patients with COPD exhibited increased bronchial resistance to the effect of the trigger factor (Pb – 1 hazard class) associated with high motorization of the city. The response of pulmonary gas exchange (SaO2 ) to Pb in the control group indicates prolonged exposure to toxic metals can cause developing respiratory failure even in the healthy population of the city.

Efficacy and Prognostic Factors of Androgen Deprivation Therapy Combined with Radiation Therapy for Prostate Cancer.

Objective To analyze the efficacy of androgen deprivation therapy (ADT) combined with radiation therapy (also known as radiotherapy) for prostate cancer. Methods The clinical data of 94 prostate cancer patients treated in the Oncology Department of Xiangzhou People's Hospital from January 2017 to January 2018 were retrospectively analyzed, and the patients were divided into the combined group and the reference group according to their admission order, with 47 cases each. The patients in the reference group only received the radiotherapy, and on this basis, those in the combined group accepted ADT, so as to evaluate the efficacy of different treatment methods by comparing the patients' serum total prostate-specific antigen (T-PSA), vascular endothelial growth factor (VEGF), and other indicators and analyze the relevant factors affecting patients' prognosis by Cox single-factor and multi-factor regression models. Results Compared with the reference group after treatment, the patients in the combined group obtained significantly lower T-PSA and VEGF levels (P < 0.001), significantly higher objective remission rate and disease control rate (P < 0.05), and remarkably longer modified progression-free survival (mPFS) and overall survival (OS) (P < 0.001), and after the multi-factor research, it was found that the Gleason score of 8–10, positive lymphatic metastasis, and single radiotherapy were the factors affecting the clinical prognosis of prostate cancer. Conclusion Combining ADT with radiotherapy ensures a better survival benefit for prostate cancer patients and has a fairly well efficacy. Further study will be conducive to establishing a better solution for such patients.

CORPORATE CULTURE OF COMPANIES IN THE CONDITIONS OF TRANSFORMATIONS

Abstract. In modern economic conditions, enterprises function and develop as a complex organism, the vital potential of which is provided by corporate culture. Corporate culture determines not only the difference between organizations, but also determines the success of their operation and survival in a transformational environment. The successful operation of the enterprise also requires a solid foundation in material, financial and human resources, however, without a well-established corporate culture, the organization will not be able to exist in today’s complex environment. In this aspect, an important role is played not only by the approaches to the diagnosis of corporate culture, which were developed by well-known scientists, but also the practical adaptation of changes in corporate culture to business practice. Among the most significant results of these studies are the method of clinical research by E. Schein, the method of diagnosing corporate culture of the organization, proposed by K. Cameron and R. Quinn, and to some extent the system of diagnostic and strategic management of the company, offered by D. Norton and R. Kaplan. The methodological basis of the study is a system of general and special methods, which allowed to investigate the impact of the phenomenon of leadership potential on corporate culture. The study was based on the use of: method of comparison, generalization — to clarify and formalize the essence of the concept; method of correlation and regression analysis — to build multifactor regression models, calculate predictive values of integrated indicators of leadership potential for each subsystem, optimize subsystems, determine the degree of influence on type of corporate culture; graphoanalytical method — to provide clarity of the material and schematic representation of a number of theoretical and practical provisions of the study. Methods of computer processing, analysis and display of information using Microsoft Excel, STATISTICA were used for complex analysis. The information base is official publications of international organizations, expert assessments of rating agencies, monographic literature, fundamental scientific developments of domestic and foreign authors, materials of scientific conferences, domestic and foreign publications, electronic resources of the Internet, etc. Keywords: corporate culture, leadership potential, diagnostics of corporate culture. JEL Classification M10, M12 Formulas: 0; fig.: 3; tabl.: 3; bibl.: 10.

SIMULATION OF LIFE CYCLE INTERVALS FOR EVALUATION OF ENTREPRENEURIAL NETWORK DEVELOPMENT

Introduction. Each stage of the life cycle of the enterprise is characterized by features in the provision of tangible and intangible resources, potential, and areas of its use, which causes differences in determining the prospects for the development of network enterprises. The article aims to determine the features of managing the development of business networks depending on their stage of the life cycle. Results. An econometric approach to determining the quantitative criteria for identifying each phase of the life cycle of network enterprises has been developed using questionnaire methods, fuzzy sets, and integrated estimation. The approbation of the proposed approach was carried out based on network enterprises of the machine-building industry of the Kharkiv region for 2015-2019. By building linear multifactor regression models, key factors-components of synergetic effect, social synergy have been defined, affecting network enterprises' efficiency. Conclusions. According to the study results, determined that at the growth stage, the priority factors of the development of network enterprises are the achievement of the synergy of strategic management, labor potential management, information, and communication synergy. At the maturity stage, enterprises' development is ensured by the positive synergetic effect of strategic management, innovation-technological and information-communication synergy. At the stage of decline by restoring the solvency of enterprises is the development of synergies of strategic management, synergies of labor potential management, innovation, and technological synergies. The emphasis on the development of these components of the synergetic effect of network interaction forms the priority areas for managing the development of network enterprises depending on their stage of the life cycle. Keywords: network enterprises, entrepreneurial network, synergetic effect, development, life cycle.

DEVELOPMENT OF MODERN BUSINESS PROCESSES AND OUTSOURCING ACTIVITIES

The purpose of the article is to study the key features of modern business processes and outsourcing. The subject-matter of the study is the methodological and conceptual foundations of the development process of modern business and outsourcing activities. Methodology. The research is based on a set of well-known general scientific and special methods of research in economics. In particular, the method of scientific abstraction, the method of systematic analysis, economic and mathematical modeling were used in the article. Conclusion. Many types of modern work can be outsourced. Thus, this will eventually lead to a confrontation between protected freelancers and freelancers whose employment is not regulated at the legislative level. The world is changing much faster, the labor market is undeniably affected when most jobs are simplified by technology; one of the consequences is a departure from the usual regulated labor relations and structures. The work environment and the environment are changing radically today. New forms of cooperation can take legal relations beyond legal regulation in those countries that fail to adapt their legislation in time to new rapidly evolving business strategies, which often lack the concept of territorial boundaries and mostly demand talent for effective solutions. With the help of constructed multifactor regression models, which are adequate to the available statistical and calculated data and have high degrees of significance of the estimated parameters, it is possible to predict changes in the simulated economic phenomenon as a result of changes in one or more factors. It should be noted that such models today are not sustainable in the long run, as in the Ukrainian and global economies, the external environment and its conditions change quite often. Therefore, in the future it is advisable to calculate new parameters of regression models based on the presented methods and make predictions according to the obtained data. In authors’ opinion, the main reason for the increase in the share of outsourcing in Europe in the coming years is the strengthening of competition in the world market and the growth of the euro. It is believed that traditionally vertically integrated business processes will be broken into fragments and partially outsourced. This will allow European companies to increase their revenues and production volumes.

Modeling of regional freight flows of road transport in Ukraine

A transport system can be defined as a complex system characterized by a random value of transport demand, variable weather and climatic factors, a set of characteristics of transport infrastructure, and a complex system of interconnections. One of the key modes of transport providing freight transport both in domestic and international traffic is road. Its mobility and the ability to deliver cargo from door to door is a unique competitive advantage over other modes of transport. To create an effective logistics infrastructure that meets the demand for domestic freight transport, first of all, information is needed on the needs for transport between regions of the country. Thus, it is necessary to look for mathematical approaches to modeling freight flows, combining their practical implementation using widely used software products (for example, MS Excel). The purpose of the paper is to build effective multifactor regression models of demand for input and output transportation of goods by road for each region of Ukraine according to publicly available statistical data of the State Statistics Service of Ukraine. The modern approach to modeling cargo flows requires fast processing of a large amount of statistical data. In addition, the method should be as universal as possible and capable of quick and simple changes under conditions of a change in statistical data. From this point of view, the most acceptable option can be considered to be the modeling of freight traffic using regression models based on correlation and regression analysis. In general, the task is to find the dependence of the demand for transportation on the factors that influence it. Such factors in the existing models are connected with various macroeconomic indicators, as well as the distance of delivery. The data of regional statistics of the State Statistics Service of Ukraine and data of the “Lardi-Trans” website as the most widely used by freight carriers and shippers were taken as the initial data for modeling. A list of factors has been found that significantly influence the demand for freight transport by road between regions of Ukraine. A rating of influencing factors has been compiled, among which are the gross regional product, regional volumes of foreign trade in goods (imports) and gross regional product per one inhabitant of the region. The absolute values of the correlation coefficients are in the range 0.351-0.974. The lowest correlation coefficient is between the transportation distance and the demand for delivery, which proves a negligible relationship between the volume of regional transportation and the distance of delivery. Multivariate regression models with thirteen, five and two factors of influence on demand are built. Accuracy parameter values are acceptable for all model variants. The normalized R-squared of the obtained models does not fall below 84%, and the average approximation error does not rise above 1.6%, which is an excellent performance of the models.

Multifactor Regression Models of Adherence to Treatment in Patients with Schizophrenia and Schizophrenic Spectrum Disorders and Their Relatives

Purpose: formation of multifactor regression models of adherence to treatment of patients with schizophrenia and schizophrenic spectrum disorders and their family members to identify the relationship with socio-demographic, clinical, psychological characteristics, family environment factors and the quality of psychiatric care in order to develop measures to improve the compliance of these groups.Patients and methods: the study involved 107 patients (44 men and 63 women), with diagnoses of schizophrenia (F.20), schizoaffective (F.25) and schizotypal disorders (F.21), eligible the ICD-10 criteria, as well as 105 of their relatives (27 men and 78 women). The following methods were used: сlinical and psychopathological, psychometric, psychological, statistical (correlation and regression analysis), methods of system analysis, method of hierarchy analysis.Results: regression models were built and factors positively or negatively influencing adherence to treatment in groups of patients and their relatives, formed depending on gender differences, awareness of the disease or its denial, as well as on the clinical characteristics of patients, were identified. Gender factors affecting adherence to treatment in men include social functioning, education and income; for women — the motivation for treatment and characteristics of family relationships. In the groups of patients and their relatives, selected depending on the awareness of the disease or its denial, the dominant factors influencing compliance were the duration of the disease, the frequency of hospitalizations, patients’ disability, the level of education of relatives, forgetfulness of taking medications, etc. In groups, depending on the clinical characteristics of patients, the compliance of patients and relatives was influenced by the age of the patient, the level of motivation for treatment of patients and their relatives, the employment of patients and the level of their social functioning, conflict, hostile relations in the family and a number of other factors.Conclusion: knowledge of the factors that affect the adherence to treatment of schizophrenia patients and their family members will contribute to the development of practical measures to improve it. Psychosocial interventions have been proposed to increase adherence to treatment in patients with schizophrenia and schizophrenic spectrum disorders and their families.

ЕКОНОМІКО-МАТЕМАТИЧНИЙ АНАЛІЗ ВПЛИВУ ІНВЕСТИЦІЙ НА РЕЗУЛЬТАТИВНІСТЬ ДІЯЛЬНОСТІ ПІДПРИЄМСТВА

Анотація. Мета. Дослідження впливу інвестицій на чистий дохід шляхом застосування багатофакторних регресійних моделей діяльності підприємства Запоріжжя. Методика. На основі економетричного аналізу розроблено і застосовано методику, що дозволяє провести аналіз впливу величини інвестиційних вкладів на чистий дохід підприємства, яка заснована на використанні багатофакторних регресійних моделей економічної діяльності підприємств. Результати. Багатогранність інформаціїпро економічну діяльність підприємства Запоріжжя вказала на складність аналізу отриманих результатів. Разом з тим перехід до цифрової економіки, яка базується на сучасних ІТ-технологіях, дав можливість по-новому використовувати статистичний матеріал за економічними показниками підприємств, синтезувавши економіко-математичні моделі підприємств як складних об’єктів шляхом більш глибокого опрацювання статистичного матеріалу з обґрунтуванням отриманих результатів. Такий підхід дав можливість за допомогою багатофакторних регресійних моделей отримати залежності чистого доходу від величини інвестиційних вкладів на конкретному підприємстві Запоріжжя. Наукова новизна. Уперше застосовано багатофакторні регресійні моделі економічної діяльності підприємств з урахуванням мультиколінеарності та нелінійності за величиною інвестиційних вкладів для економічного аналізу впливу інвестицій на чистий дохід шляхом використання коефіцієнта еластичності. Практична значимість. Аналіз результатів економіко-математичного моделювання діяльності підприємства Запоріжжя, який ґрунтується на статистичному матеріалі із застосуванням алгоритму дослідження залежності чистого доходу від величини інвестиційного внеску, показав наявність критичних величин інвестиційних вкладів, які якісно змінюють вплив на чистий дохід, що підтверджується знаком коефіцієнта еластичності. Рекомендується включити розроблений алгоритм в практику відповідних розрахунків результативності економічної діяльності підприємств. Ключові слова: чистий дохід, інвестиційні вклади, регресія, багатофакторна модель, мультиколінеарності, еластичність. Формул: 8; рис.: 1; табл.: 1; бібл.: 16..

Marketing and management in insurance: impact of innovations measures

Nowadays insurance industry has huge innovation potential. Several key vectors for developing the concept of insurance tech include machine learning, business analytics, consumer protection rules, Big Data, artificial intelligence, neural networks, blockchain, and telematics. Technological innovations become widespread only when a community that supports them emerges, and COVID-19 has rapidly accelerated the changes that were already in full swing to a greater extent than any other factor. COVID-19 has helped reinforce the story and illustrate the results that technologies achieve on a large scale. Modern marketing and management approaches in insurance are viewed as an activity to optimize and control the insurance company's innovation and marketing activities. It would allow taking a strategically advantageous position in the insurance market. There are two kinds of insurance marketing: structural and commodity. Structural marketing could help to solve the problem of the economic efficiency of the activity of insurance companies. Commodity marketing helps to improve financial activity and, as a result, to increase profitability. This article summarizes the arguments and counterarguments within the scientific discussion on the place and prospects marketing and management in insurance (strategies, functions, principles) in the context of key innovation metrics. The study's primary purpose is to confirm the hypothesis about the functional link between the level of innovative development of the country and key insurance determinants as drivers for transformation in marketing strategies of insurance companies. In this regard, the array of input data is presented in the form of seven independent variables (regressors), six of which denote innovation measures, one is control variable, and five dependent variables (regressands), which identify the insurance sector. The study of the impact of innovation metrics on the insurance sector of the country in the article is carried out in the following logical sequence: 1) the formation of an array of input data; selection of relevant indicators using Principal Component Analysis; 2) formalization of functional relationships between variables by constructing five-panel Multifactor regression models with Random Effects; and 3) interpretation of the obtained results. Seventeen countries of Central and Eastern Europe were selected as the object of the study for the period from 2004 till 2019. The study empirically confirms the above hypothesis, which is evidenced by the following identified dependences. Key insurance determinants depend on innovation fluctuations. The most significant positive influence on the dependent variables is exercised by the Innovations index, Research and development expenditure, and Patent applications by residents. The study results could be helpful for insurance companies that provide new insurance technologies and seek to optimize activities to support innovative development. The main directions of marketing and management in insurance should be considered from two positions applying new technologies in insurance marketing and introducing new insurance products or services.

Нейромережевий аналіз термограм молочної залози з використанням оцінки фрактальної розмірності розподілу поля температур

Introduction. Thermography is one of the promising additional standard methods of mammary glands screening in a large group of population. This method is considered to be suitable for widespread use due to its non-invasiveness, lack of radiation exposure and thus safety for the health of patients, accessibility to patients and high detection effectiveness of pathological changes of the mam-mary gland. Methods of thermograms evaluation and analysis. To identify the risk of mammary gland pathology we analyzed thermograms using 68 features, among which three indicators of general characteristics: age of the patient, minimal temperature of theMG field, size of the MG temperature field; 32 features of the relative area of temperature rise; and 33 features of thermograms characteris-tics according to Hurst exponent of high dimensional fractals. To analyze distribution of MG field temperature and to identify signs of thermograms associated with the risk of pathology, methods of constructing one-factor and multifactor regression models were used, as well as method of operating characteristic curves (ROC). Quantitative analysis of the thermography results. On the basis of the selected factor signs, a linear model for predicting the risk of MG pathology was built — AUC = 0,85 (95% CI 0,82–0,87) and a nonlinear model (was used a multilayer perceptron — MLP, with one hidden layer with sigmoid activation functions) for predicting the risk of MG pathology AUC = 0,89 (95% CI 0,87–0,92). A non-linear neural network model on a reduced set of traits had better (p &lt; 0,05) prognostic characteristics (AUC) than a linear model on all 68 features or a linear model on significant factor features. The prognostic characteristics of the MLP model allow to use it in order to predict the risk of a pathological process. Conclusions. To analyze mammary gland thermograms with assessment of the fractal dimension of the field temperature distribu-tion in norm and in pathology was constructed a neural network MLP model for predicting the risk of MG pathology. Sensitivity of the proposed model is 90,2% (95% CI 86,7%–93,0%), specificity — 85,1% (95% CI 80,6%–88,9). Key words: Thermograms; Mammary gland; Fractal dimension; MG temperature; One-wayanalysis of variance; MLP model.