Statistical Modeling Methods Research Articles

Excel-oriented calculator for calculating results of entropy analysis of data distributed by categories

The goal of the work. Development of EXCEL-oriented calculator for calculating the results of entropy analysis of data, which are distributed by categories. The subject of research is histograms of arbitrary distribution laws and conjugation tables 2×2. Research methods: Entropy and information analysis of histograms of arbitrary distribution laws and conjugation tables. The obtained results. It is proposed to use methods of entropy analysis for the analysis of data distributed by categories; information on the structure of the EXCEL-oriented calculator designed for this purpose is given. The calculator makes it possible to calculate entropy characteristics of histograms, namely: histogram entropy, histogram dispersion, histogram confidence intervals, diversity information index. The calculator performs a pairwise comparison of entropies of histograms using the Hutcheson method, determines Hellinger and Kullback-Leibler distances between histograms of arbitrary distribution laws and thus complements the chi-square criterion, determines the informational correlation coefficient. The correspondence between the Pearson correlation coefficient and the information correlation coefficient is established by the method of statistical modeling. For 2×2 conjugation tables, the calculator makes it possible to estimate the significance of the interaction between the row factor and the column factor. The calculator determines the values of conditional entropies for 2×2 conjugation tables. The proposed calculator fills the gaps in existing software products and can be used to process data distributed by categories using entropy analysis methods. It is shown that entropy methods of analysis are appropriate to use in cases where histograms determine arbitrary distribution laws.

Synthesis of quasi-optimal fast filters by the least square criterion

The subject of the article research is special signal processing methods based on the optimal discrete filtering theory. The goal is to increase the efficiency of model-based methods for processing information signals by reducing computational costs and increasing the speed of optimal discrete filtering algorithms. Applied methods: description of dynamic processes in terms of state space using elements of vector-matrix algebra, weighted least squares method, elements of Kalman's theory of optimal discrete filtering, basic concepts of the O'Reilly–Luenberger theory of functional observers, elements of probability theory, statistical modeling by the Monte Carlo method. Results: a new method for reducing computational costs is proposed, which uses the approximation of the Kalman filter transfer matrix time dependence by given piecewise linear functions according to the least squares criterion. The effectiveness of the method was evaluated on the example of a second-order dynamical system. On the basis of a comparative analysis, several acceptable variants of the considered approximation are proposed. The practical significance of the work lies in the further development of methods for the synthesis of quasi-optimal high-speed filters. The operability of the proposed modifications is confirmed by the example of a second-order linear dynamic system. The efficiency of the algorithms was evaluated by the statistical modeling method according to the criterion "accuracy-computational costs". It is shown that the total savings in the number of multiplication and addition operations can reach tens of times due to insignificant losses in the accuracy of the filtering process.

Misspecification and unreliable interpretations in psychology and social science.

The replicability crisis has drawn attention to numerous weaknesses in psychology and social science research practice. In this work we focus on three issues that cannot be addressed with replication alone, and which deserve more attention: Functional misspecification, structural misspecification, and unreliable interpretation of results. We demonstrate a number of possible consequences via simulation, and provide recommendations for researchers to improve their research practice. Psychologists and social scientists should engage with these areas of analytical and statistical improvement, as they have the potential to seriously hinder scientific progress. Every research question and hypothesis may present its own unique challenges, and it is only through an awareness and understanding of varied statistical methods for predictive and causal modeling, that researchers will have the tools with which to appropriately address them. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Analysis of trade-offs/synergies in land use function changes in Bin County, China

Land use function has become an important part in land change science. More attention should be given to multifunctional land to achieve land structure optimization and sustainable land use. This study takes Bin County in Heilongjiang province of China as the research area and investigates the spatiotemporal pattern and trade-off synergistic relationship of land functions from 2000 to 2020. Land functions were quantitatively measured by the spatiotemporal method of statistical data, INVEST model, CASA, RUSLE, and other models. The results showed that 1) during 2000–2020, the production function showed an overall trend of enhancement. Living function remained enhanced except in the southeast and north. The water conservation function was weakened in most areas, while the carbon fixation function was strengthened in the southeast and weakened in the central and northern parts. Soil conservation function decreased first and then increased significantly in the southwest. 2) There was a synergistic relationship between production and living functions and trade-off relationships between ecology and production functions and ecology and living functions from 2000 to 2020. 3) The production and living functions are mainly coordinated in space, and the collaborative agglomeration types are mainly distributed in Tangfang town, Manjing town, and other areas. The spatial distribution of trade-offs and synergies between ecological and productive functions and ecological and living functions was similar. The trade-off and agglomeration types were scattered, and the synergies were distributed in Binxi town and Juren town.

Analysis and modelling of thrust force in drilling of basalt and carbon fibre-reinforced polymer (BFRP and CFRP) composites

Currently, the use of sustainable products and technologies is growing; consequently, mineral-origin basalt fibre-reinforced polymer (BFRP) composites are becoming more popular in industries. Although BFRP parts require mechanical drilling operations for manufacturing holes for assembly, many challenges make the drilling process difficult. Considering that the cutting force is one of the main parameters characterising the drilling process, this study aims to analyse the influence of feed (mm/rev) and cutting speed (m/min) on the thrust force and model the thrust force in the drilling of BFRP composites through response surface methodology (RSM) and advanced statistical modelling methods. In order to determine main and interaction effects and to calculate the regression coefficients and model parameters, mechanical drilling experiments were performed, and the thrust force was recorded. The raw force data were processed using fast Fourier transformation-based low-pass filtering, and then the calculated thrust force parameters were evaluated relative to various feeds and cutting speeds. In addition, results were compared with those of carbon fibre-reinforced polymer composites. The results of the validation experiments show that both RSM and advanced statistical models accurately predict the thrust force in BFRPs of 96.74% and 95.01%, respectively. However, the advanced statistical model can describe not only the maximum values of the force but also its characteristics at a coefficient of determination of 0.68.

Morphological Variation of the Mandible in the Orthognathic Population-A Morphological Study Using Statistical Shape Modelling.

The aim of this study was to investigate the value of 3D Statistical Shape Modelling for orthognathic surgery planning. The goal was to objectify shape variations in the orthognathic population and differences between male and female patients by means of a statistical shape modelling method. Pre-operative CBCT scans of patients for whom 3D Virtual Surgical Plans (3D VSP) were developed at the University Medical Center Groningen between 2019 and 2020 were included. Automatic segmentation algorithms were used to create 3D models of the mandibles, and the statistical shape model was built through principal component analysis. Unpaired t-tests were performed to compare the principal components of the male and female models. A total of 194 patients (130 females and 64 males) were included. The mandibular shape could be visually described by the first five principal components: (1) The height of the mandibular ramus and condyles, (2) the variation in the gonial angle of the mandible, (3) the width of the ramus and the anterior/posterior projection of the chin, (4) the lateral projection of the mandible's angle, and (5) the lateral slope of the ramus and the inter-condylar distance. The statistical test showed significant differences between male and female mandibular shapes in 10 principal components. This study demonstrates the feasibility of using statistical shape modelling to inform physicians about mandible shape variations and relevant differences between male and female mandibles. The information obtained from this study could be used to quantify masculine and feminine mandibular shape aspects and to improve surgical planning for mandibular shape manipulations.

Determining psycholinguistic features of deception in phishing messages

PurposeDistinguishing phishing emails from legitimate emails continues to be a difficult task for most individuals. This study aims to investigate the psycholinguistic factors associated with deception in phishing email text and their effect on end-user ability to discriminate phishing emails from legitimate emails.Design/methodology/approachEmail messages and end-user decisions collected from a laboratory phishing study were validated and analyzed using natural language processing methods (Linguistic Inquiry Word Count) and penalized regression models (LASSO and Elastic Net) to determine the linguistic dimensions that attackers may use in phishing emails to deceive end-users and measure the impact of such choices on end-user susceptibility to phishing.FindingsWe found that most participants, who played the role of a phisher in the study, chose to deceive their end-user targets by pretending to be a familiar individual and presenting time pressure or deadlines. Results show that use of words conveying certainty (e.g. always, never) and work-related features in the phishing messages predicted higher end-user vulnerability. On the contrary, use of words that convey achievement (e.g. earn, win) or reward (cash, money) in the phishing messages predicted lower end-user vulnerability because such features are usually observed in scam-like messages.Practical implicationsInsights from this research show that analyzing emails for psycholinguistic features associated with computer-mediated deception could be used to fine-tune and improve spam and phishing detection technologies. This research also informs the kinds of phishing attacks that must be prioritized in antiphishing training programs.Originality/valueApplying natural language processing and statistical modeling methods to analyze results from a laboratory phishing experiment to understand deception from both attacker and end-user is novel. Furthermore, results from this work advance our understanding of the linguistic factors associated with deception in phishing email text and its impact on end-user susceptibility.

Digital Assets and the Global Economy: How the Use of Statistical Models Can Help Bitcoin Price Prediction

The purpose of the study is to analyze the potential of statistical modeling in predicting the prices of the Bitcoin cryptocurrency and its impact on the economy. In the course of the article, answers were received to such questions as: What is the impact of macroeconomic events on the dynamics of the Bitcoin price? How quickly does the cryptocurrency market stabilize after the falls? How effective is statistical modeling to solve the problem of predicting the price of Bitcoin? Which model shows the best results? What measures of regulation and control of the cryptocurrency market are necessary at the stage of its formation in the Russian Federation?Materials and methods. Historical data on average monthly Bitcoin closing prices and macroeconomic events such as the COVID-19 pandemic and the Russian-Ukrainian conflict were collected and analyzed. The paper uses statistical models, including ARIMA and LSTM, to predict future Bitcoin prices based on historical data. The accuracy of the models was calculated based on such indexes as the mean absolute error (MAE) and the mean square error (MSE). Results. Analysis of the impact of macroeconomic events showed that during the crisis, the attractiveness of Bitcoin increased and investors used this asset as a new investment tool. During the analysis of the consequences of the Russian-Ukrainian conflict for the cryptocurrency market, its reaction to geopolitical events was revealed according to the increased liquidity indexes in the market. In the process of modeling the dynamics of the average monthly Bitcoin price, the model with parameters (1, 1, 0) at MAE = 15.03% was recognized as the best ARIMA model. The LSTM neural network model on a similar data set showed a MAE error equal to 2.57%.Conclusion. The analysis shows that itcoin was the most attractive investment tool during the crisis, which led to a sharp increase in its price in 2021. The Russian-Ukrainian conflict has also affected its price, causing a significant decline in 2022. However, statistical modeling methods predict an increase in the price of Bitcoin in the first half of 2023, and governments may consider regulating or controlling its use to reduce risks associated with the cryptocurrency market. The recommended measures are the introduction of regulations, the introduction of transaction taxes, the development of national digital currencies, public education and the prevention of criminal activity.

Daily tourism demand forecasting before and during COVID-19: data predictivity and an improved decomposition-ensemble framework

ABSTRACT The global tourism industry is struggling to recover from the COVID-19 pandemic. During the COVID-19 pandemic, daily tourism forecasting is more critical than ever before in supporting decisions and planning. Considering the changes in tourist psyche and behaviour caused by COVID-19, this study attempts to investigate whether the statistical modelling methods can work reliably under the new normal when travel restrictions are eased or lifted. To this end, we first compare the predictivity of daily tourism demand data before and during COVID-19, and observe heterogeneous impacts across different geographical scales. Then an improved multivariate & multiscale decomposition-ensemble framework is proposed to forecast daily tourism demand. The empirical study indicates the superiority and practicability of the proposed framework before and during COVID-19. Finally, we call for more research on the comparability of tourism demand forecasting.

Федеральный бюджет и бюджетно-налоговая политика государства: макроэкономическая адаптация до 2025 года

The article examines the modern concept of the state fiscal policy in Russia and the possibilities of its implementation through the federal budget in the face of new global challenges. To this end, the authors assessed the resource base of the federal budget of the Russian Federation, as well as the adequacy of measures of fiscal regulation of the current economic situation. In this regard, the authors evaluated the feasibility of the initial macroeconomic indicators underlying the parameters of the federal budget for 2023–2025, primarily its revenues, and the risks of their possible change. The authors associate the key risks of failure to achieve the stated goals of the budget and tax policy for the period 2023–2025 with a sharp reduction in the resource base of the federal budget, especially its oil and gas revenues in the context of worsening geopolitical tensions and increasing sanctions pressure on the Russian Federation. The study focuses on the justification of approaches to minimize the negative consequences of external shocks both for the subjects of the Russian economy and for public finances. In the course of the study, the authors used the methods of statistical data processing, factor modeling, regression analysis and expert evaluation. The scientific novelty of the study lies in the assessment of the impact of new global challenges on the balance of the Russian federal budget and substantiation of approaches to transform fiscal regulation measures in response to these challenges. The practical significance of the study lies in the possibility of using its results in the development of state fiscal policy in the medium term under the conditions of dramatic changes in the scenario conditions of formation of the federal budget.

A prediction model based on an adaptive genetic algorithm and optimized Elman neural network for PV power

Due to the high volatility and randomness of PV power generation, fast change frequency, limited data samples and poor data conditions, the reliability, stability and economy of grid operation are affected seriously. Therefore, the accurate prediction of PV power generation can mitigate the impact of its volatility on the stability of national grid power operations. And it improves PV consumption capacity and enhances the security of microgrid and distribution grid interconnection. This paper first proposed a PV power prediction method. Secondly, through the introduction and analysis of three types of prediction methods, the first type of prediction is selected using statistical analysis and modeling methods. Thirdly, an optimized Elman neural network-based PV power prediction model is proposed to allow accurate prediction of PV power prediction. Finally, an example is given to verify the advantages of the algorithm.

Dynamic multi feature-class Gaussian process models.

In model-based medical image analysis, three relevant features are the shape of structures of interest, their relative pose, and image intensity profiles representative of some physical properties. Often, these features are modelled separately through statistical models by decomposing the object's features into a set of basis functions through principal geodesic analysis or principal component analysis. However, analysing articulated objects in an image using independent single object models may lead to large uncertainties and impingement, especially around organ boundaries. Questions that come to mind are the feasibility of building a unique model that combines all three features of interest in the same statistical space, and what advantages can be gained for image analysis. This study presents a statistical modelling method for automatic analysis of shape, pose and intensity features in medical images which we call the Dynamic multi feature-class Gaussian process models (DMFC-GPM). The DMFC-GPM is a Gaussian process (GP)-based model with a shared latent space that encodes linear and non-linear variations. Our method is defined in a continuous domain with a principled way to represent shape, pose and intensity feature-classes in a linear space, based on deformation fields. A deformation field-based metric is adapted in the method for modelling shape and intensity variation as well as for comparing rigid transformations (pose). Moreover, DMFC-GPMs inherit properties intrinsic to GPs including marginalisation and regression. Furthermore, they allow for adding additional pose variability on top of those obtained from the image acquisition process; what we term as permutation modelling. For image analysis tasks using DMFC-GPMs, we adapt Metropolis-Hastings algorithms making the prediction of features fully probabilistic. We validate the method using controlled synthetic data and we perform experiments on bone structures from CT images of the shoulder to illustrate the efficacy of the model for pose and shape prediction. The model performance results suggest that this new modelling paradigm is robust, accurate, accessible, and has potential applications in a multitude of scenarios including the management of musculoskeletal disorders, clinical decision making and image processing.

ON THE COMPARATIVE EFFICIENCY OF CHANGE POINT DETECTION IN MULTIVARIATE TECHNOLOGICAL PROCESSES USING MULTIDIMENSIONAL DOUBLE CONTROL CHARTS

The problem of change point detection in multiparametric technological processes having a normal distribution and consisting in a shift from a given value of the sample mean and sample variance is investigated. Various types of control charts are considered, which make it possible to effectively detect simultaneous changes in the mean value and variance in multiparametric technological processes. By the method of statistical modeling, an analysis of the comparative effectiveness of control charts is carried out, practical recommendations are given.

Research on propeller cavitation wake characteristics based on multivariate statistical modeling method

The radiation noise generated by cavitation has been extensively studied for underwater target recognition, but there are few reports on the related mechanism of the cavitation noise of ship propellers that attract attention in the field of hydroacoustics. In this paper, the RANS equations of the underwater propeller wake field are constructed, and numerically solved by combining the cavitation model and the turbulence model. The power spectrum is used to analyze the signal of the numerical calculation results of the propeller wake pressure. The feature estimation and extraction are carried out to obtain the characteristic values of the specific characteristic parameters. These eigenvalues not only reflect the flow field characteristics but also the geometric parameters and working conditions of the propeller. Therefore, two models are established around the relationship between them. Firstly, these eigenvalues are used for regression analysis in multivariate statistics to obtain a statistical model reflecting the characteristics of propeller cavitation wake. Secondly, the relationship between the propeller skew angle and the low frequency linear spectrum amplitude is obtained by using the power spectrum diagram. In this paper, the processing results of the experimental data of the cavitation water tunnel with controllable parameters and the radiation noise data of the actual target are used to verify and supplement each other with the processing results of the feature model.

Toward a comprehensive catalog of regulatory elements.

Regulatory elements are the genomic regions that interact with transcription factors to control cell-type-specific gene expression in different cellular environments. A precise and complete catalog of functional elements encoded by the human genome is key to understanding mammalian gene regulation. Here, we review the current state of regulatory element annotation. We first provide an overview of assays for characterizing functional elements, including genome, epigenome, transcriptome, three-dimensional chromatin interaction, and functional validation assays. We then discuss computational methods for defining regulatory elements, including peak-calling and other statistical modeling methods. Finally, we introduce several high-quality lists of regulatory element annotations and suggest potential future directions.

A Data-Science Approach for Creation of a Comprehensive Model to Assess the Impact of Mobile Technologies on Humans

Mobile technologies are an essential part of people’s everyday lives since they are utilized for a variety of purposes, such as communication, entertainment, commerce, and education. However, when these gadgets are misused, the human body is exposed to continuous radiation from the electromagnetic field created by them. The communication services available are improving as mobile technologies advance; however, the problem is becoming more severe as the frequency range of mobile devices expands. To solve this complex case, it is necessary to propose a comprehensive approach that combines and processes data obtained from different types of research and sources of information, such as thermal imaging, electroencephalograms, computer models, and surveys. In the present article, a complex model for the processing and analysis of heterogeneous data is proposed based on mathematical and statistical methods in order to study the problem of electromagnetic radiation from mobile devices in-depth. Data science selection/preprocessing is one of the most important aspects of data and knowledge processing aiming at successful and effective analysis and data fusion from many sources. Special types of logic-based binding and pointing constraints are considered for data/knowledge selection applications. The proposed logic-based statistical modeling method provides both algorithmic as well as data-driven realizations that can be evolutionary. As a result, non-anticipated and collateral data/features can be processed if their role in the selected/constrained area is significant. In this research, the data-driven part does not use artificial neural networks; however, this combination was successfully applied in the past. It is an independent subsystem maintaining control of both the statistical and machine-learning parts. The proposed modeling applies to a wide range of reasoning/smart systems.

Comparative Evaluation of the Possibilities of Radiomic Аnalysis of Magnetic Resonance Imaging in the Differential Diagnostics of Primary Extra-Axial Intracranial Tumors

Purpose of the study. Comparing magnetic resonance imaging (MRI) abilities in differential diagnostic of three types of primary extra‑ axial brain tumors (benign and malignant meningiomas, and neuromas) based on standard semiotics and radiomic features.Patients and methods. Retrospective research included 66 patients with primary extra‑a xial tumors who were divided into two groups: the instructional (39 patients) and the valid (27 patients). MRI was used towards all patients before surgery. The one method of statistical modeling – discriminant analysis – was used to compare the abilities of differential diagnostic based on semiotic features and radiomic parameters.Results. The features of tumor semiotics MRI didn’t allow to differentiate effectively benign and malignant meningiomas. Several parameters were certainly varied for all those tumor types (neuromas, benign and malignant meningiomas). The modelling based on the discriminant analysis demonstrated that radiomic features can be used for primary extra‑a xial tumors differential diagnostic. The area of the radiomic model ROC‑curve took 0.86 which exceeds the result of the model based on semiotic features (AUC 0.78).Conclusion. The best results of the tumors classification by radiomic model demonstrate expediency to continue research the primary extra‑ axial tumors differential diagnostic with support of histogram and textural parameters of MRI imaging.

Development of methodology for economic evaluation of land plots for the extraction and processing of solid minerals

The Russian economy has a pronounced resource specialization; in many regions, subsoil use is a backbone or one of the main areas of the economy. In these conditions, the improvement of the methodology for the economic evaluation of lands on which mining enterprises are located is of particular relevance. On the basis of the existing experience in assessing industrial land, the authors present a developed methodology for determining the cadastral value of land plots where mining enterprises are located, taking into account their industry characteristics and the specifics of production and commercial activities. At the same time, cadastral valuation is considered as a specific form of economic valuation. Particular emphasis is placed on the importance of taking into account the cost factors that have the greatest impact on the formation of the cost of land for the extraction and processing of solid minerals, and the method of assessment depending on the characteristics of the object. To carry out theoretical research, the methods of analysis and synthesis of legal and scientific and technical literature in the field of cadastral and market valuation of land were used in the work. The practical part is based on the application of expert methods, including the method of analysis of hierarchies, system and logical analysis. The method of M.A.Svitelskaya was chosen as the basis, which presents a combination of modified methods of statistical (regression) modeling and modeling based on specific indicators of cadastral value. The use of this technique in economic practice contributes to increasing the efficiency of cadastral valuation and the objectivity of its results.

QUALITY MANAGEMENT OF QUARTZ RESONATORS

The formation of various devices of radio electronics and communication technology in general is associated with an increase in its reliability, the stability of the generated frequencies, and the selectivity of the receiving equipment. The problem of increasing the reliability of communication has been partially solved with the help of quartz resonators, which are widely used in radio circuits. The use of quartz resonators in electric generators allows to guarantee high frequency stability without complicating the circuit. The world trend in the development of modern electrical equipment is associated with miniaturization, quartz resonators in their dimensions are decreasing every year. At the same time, the requirements for reliability characteristics remain high. The purpose of this work is to evaluate the possibility of using quartz resonators presented in a miniature ceramic package with dimensions of 2.5×2.0×0.6 mm, in conditions with increased requirements for ambient temperature, as well as to select the most optimal requirements for thermal training mode, which is the basic technological operation for stabilizing the vibration frequency. Testing quartz resonators for reliability and methods of statistical modeling in radio engineering. Requirements for the reliability characteristics of quartz resonators RK588 in miniature ceramic cases 2.5×2.0×0.6 mm in size when exposed to an elevated ambient temperature of +85 C and +125 C. Requirements for frequency when exposed to increased ambient temperature on the type of crystal plate, created on the basis of RF patent No. 27122426 “Method for the manufacture of thin crystal plates and thin crystal elements”. The method of thermal training has been optimized and the aging coefficients have been established.

The Behavioral and Social Dimension of the Public Health System of European Countries: Descriptive, Canonical, and Factor Analysis.

The state and prospects of the healthcare industry of a country are among its top priorities because the quality of life and health of its citizens are indicators of its success and competitiveness. The aim of this study is to conduct a theoretical analysis and qualitative and quantitative assessments of indicators by developing an integral indicator in the context of behavioral, social, demographic, and economic factors that characterize the level of healthcare system development in European countries using multivariate statistical modeling methods. The study was implemented using Statistica 10 and Statistica Portable statistical packages. The statistical base of the study was formed using descriptive analysis; a group of 10 European countries was identified using a cluster analysis based on the application of an iterative divisive k-means method. The degree and significance of the interrelations between the components characterizing the studied groups of indicators were determined using canonical correlations by conducting a canonical analysis. Factor modeling is conducted by applying the analysis of the main components to determine the relevant indicators for assessing the level of healthcare system development to build integral indicators of the level of healthcare system development in European countries. The need to improve the level of healthcare system development in European countries was confirmed. Shortcomings and possible reserves for potential improvement of the healthcare system were identified. The results can help public authorities, officials and employees of the healthcare sector organize and conduct effective, timely, high-quality regulation and adjustment of the regulatory and legislative framework to improve healthcare system development.