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Abstract

Cadastral valuation of forestlands is poorly developed in Russia. Current methods of evaluation either depend on the probability of harvesting or do not differentiate forest areas by forest stand properties. In this study authors propose to use forest inventory data as a basis for cadastral evaluation of forestlands. At first, forest inventory data is reviewed and variables making the largest contribution to evaluation are determined using correlation matrix. Second, forest inventory data is brought to common comparison year using regression equations of stand development. After that, graphic presentation of cadastral value dependence on inventory data is visually analysed. Results of analysis allow calculating of relative value of forestland and get to absolute value using average regional cost index. Evaluation results correlate with Faustmann method.

Abstract

The utilization of renewable energy sources has an increasing role in the EU’s climate and energy policy. There are several reasons for increasing the use of renewable energy. The motives are the reduction of imported dependence on fossil fuels, mitigation of the adverse environmental impact of the energy sector and boosting of industrial development. The study provides a comprehensive overview on the structure and utilization of energy production of the Visegrad countries, focusing on the dependence on energy imports. The purpose of the article is to analyze the gross inland energy consumption of the Visegrad countries and to examine the relationship between renewables and non-renewable energy sources. In the course of the analysis, we tried to find out which non-renewable energy carrier is replaced by the renewables.

evaluated possible problems of multicollinearity, and variables with a value lower than 0.5 on the diagonal were removed, which yielded a reduced and appropriate number of factors. The factorial analysis was considered adequate with a value in the determinant of .009 in the correlation matrix. The null hypothesis in the Bartlett sphericity test was also rejected ( p = .007) and we confirmed the sample adequacy with the Kaiser-Meyer-Olkin test (KMO = .626). The anti-image matrix of the correlation matrix was analysed to identify the variables with the lowest coefficient

Abstract

De Finetti theorem establishes the conceptual basis of Bayesian inference replacing the independent and identically distributed sampling hypothesis prevalent in frequentist statistics with the much easier to justify in practical settings hypothesis of exchangeability. In this paper we make use of the extension of the concept of exchangeability from sequences to arrays arguing that the invariance to ordering is a much more tenable assumption than independent and identically distributed sampling in the financial modeling problems. Making use of the celebrated Aldous-Hoover representation theorem of exchangeable matrix we construct a Bayesian non-parametric model of the financial returns correlation matrices arguing that a Bayesian approach can mitigate many of the known shortcomings of the usual Pearson correlation coefficient. We posit the correlation matrix to be an exchangeable matrix and construct a Bayesian neural network to estimate the functions from the Aldous-Hoover representation theorem. The correlation matrix model is coupled with a Student-t likelihood (accounting for the heavy tails of financial returns). The model is estimated with a Hamiltonian Monte Carlo sampler. The samples are used to construct an ensemble of networks where each edge is weighted by the size of the correlation between two financial instruments. Various centrality measures are being calculated (betweenness, eigenvector) for each network of the ensemble allowing us to obtain a probabilistic view of each financial instrument’s importance. We also construct a minimum spanning tree associated with the mean correlation matrix allowing us to visualize the most important financial instruments from the universe selected.

Abstract

This study investigated the possibility of using artificial neural networks to predict changes in the concentration of chloride ions in the urban ponds on the example of the inflow and outflow zones of water to and from the ponds Syrenie Stawy in Szczecin (NW-Poland). The possibility of using selected water quality indices (selected based on correlation matrix of water quality indices with Cl), in particular: COD-Cr, BOD5, DO, water saturation by O2 and NO2 and their influence on the chloride concentration forecast was tested.

Abstract

The aim of the paper is to identify a potential linear correlation between direct taxes and economic growth. The subject of the paper includes estimating the level and intensity of correlation between direct taxes and economic growth in OECD countries for the period 1996-2016. The study analyses tax forms such as personal income tax, corporate income tax and tax on property, and their potential relationship with economic growth, measured by GDP growth rate. Also, tax revenues growth has been included to determine whether it directly affects the economic growth in observed countries. The results of the group correlation matrix have shown that there is a statistically significant relationship between tax revenues growth, personal income tax, corporate income tax and gross domestic product in OECD countries. However, it is important to note that tax on property and gross domestic product are not significantly correlated at the OECD level, which is logical given the low share of this tax in those countries.

Abstract

In parallel studies, different regression models were tested to identify relationships between particular dendrobiometrical indicators on two sample plots representing forests dominated by the European beech in the Central Balkan Range (Bulgaria). The presence of incomplete multicollinearity was studied through correlation matrix for factor variables. To avoid multicollinear negative impact, step multiple regression was applied and adequate regression equations of the relationships under consideration were formulated. The results of statistical analysis confirmed that the link between the investigated indicators is strong and that the ’cloud‘ data show some ’sphericity‘ and distribution close to normal. In one of the sample plots, one major volume-forming factor – height does not participate in the obtained regression equation, so it is not possible to estimate its influence. By testing linear and several nonlinear regression dependencies and by mediating widely used statistical criterions for model selection, the optimal linear model of the considered link was chosen.

Abstract

The present paper examines the psychometric properties of the Romanian version of the Irrational Procrastination Scale (IPS). The Principal Component Analysis for IPS revealed a two factors structure, but the second factor is loaded by an item that refers to postponing tasks and another item that actually expresses the opposite behavior. We therefore conclude that IPS is in fact a one-dimensional construct, as the author of the scale suggested. The IPS has good reliability. The correlation matrix indicated that the procrastination scale did correlate weakly with measures of selfefficiency and relf-regulation but it was higly correlated with factor H, factor O and global scale Q4 from Cattell’s 16 PF personality questionnaire. As a conclusion, the Romanian translation of the Irrational Procrastination Scale is a general measure of procrastination as irrational delay which can be successfully used in student populations.

Abstract

This study concerns the problem of late complications of antineo-plastic therapy. Reduced parameters of the cardiorespiratory system in childhood may have a tremendous impact on health. In order to assess the selected parameters, to evaluate physical endurance, and compare the results with those obtained for healthy children, a test was carried out on a treadmill, until 80% of maximum pulse rate was reached. To compare the differences between the treatment group and the control group, three approaches were used. The first one was the classical statistical inference, the second consisted in forming a multidimensional normal model and also involved modelling of the correlation between variables. The unstructured type of the working correlation matrix was chosen to obtain the results and correct standard errors. In the last approach, logistic regression was used to model the relationship between binary outcome and covariates, and to differentiate between the groups of patients on the basis of their cardiovascular parameters.

Abstract

In this paper, choosing highly frequent keywords from core journals in the field of 1992-2013 national knowledge discovery in CNKI database, counting the number of two frequent keywords co-occurrences in the same journal, then constructing the highly frequent keywords matrix, and transforming the highly frequent keywords matrix into a correlation matrix and a dissimilarity matrix, we analyze the dissimilarity matrix based on the use of factor analysis, cluster analysis. After discussing the results of the analysis, we found that the current hotspots in the field of domestic knowledge discovery have focused on the following six aspects, knowledge discovery based on data research, knowledge discovery algorithm optimization research, the model of knowledge discovery and references research, knowledge management based on domain ontology, expert system construction research, and applied research of the knowledge discovery. Finally, we summarized the research hotspots in the field of international knowledge discovery in the same way and suggested the domestic scholars to extend some directions of the research in the field of knowledge discovery.