Statistics in Transition最新文献

{"title":"Assessing the maturity of the current global system for combating financial and\u0000 cyber fraud","authors":"O. Kuzmenko, H. Yarovenko, L. Perkhun","doi":"10.59170/stattrans-2023-013","DOIUrl":"https://doi.org/10.59170/stattrans-2023-013","url":null,"abstract":"The purpose of the article is to assess the maturity of systems for counteracting\u0000 financial and cyber fraud with the view of their future integration at global-level. The\u0000 calculations made by the authors were based on indicators for 76 countries, which\u0000 characterized each country's level of cybersecurity and its ability to combat financial\u0000 fraud in 2018. After optimising the input data and selecting relevant indicators, the\u0000 authors built an integrated cybersecurity index using the Sundarovsky convolution\u0000 method. Sigma-limited parameterisation and Pareto-optimisation were then used to\u0000 identify the determinants of the ability to counter financial and cyber fraud, which\u0000 were used as predictors. Nonlinear regression was applied to determine the dependency of\u0000 the integrated cybersecurity index on the government efficiency index, the ease of doing\u0000 business and on the crime indices. On this basis, the authors conducted a bifurcation\u0000 analysis of the maturity of current global system for combating financial and cyber\u0000 fraud and produced its phase portraits. It was found to be mature (“Government\u0000 Efficiency Index – Ease of Doing Business” and “Ease of Doing Business – Crime Index”)\u0000 and insufficient mature (“Government Efficiency Index – Crime Index”), with the\u0000 components' imbalance indicating high system's sensitivity to react on changes. The\u0000 constructed 'Equilibrium States' phase portraits showed non-equilibrium phase portraits\u0000 of the 'saddle' type. The obtained results made it possible to identify determinants of\u0000 a global integrated system's instability to combat financial and cyber fraud.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41429688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of the principal component method and parallel analysis in\u0000 working with official statistical data","authors":"Halyna Holubova","doi":"10.59170/stattrans-2023-011","DOIUrl":"https://doi.org/10.59170/stattrans-2023-011","url":null,"abstract":"The dynamic development of the digitized society generates large-scale information\u0000 data flows. Therefore, data need to be compressed in a way allowing its content to\u0000 remain complete and informative. In order for the above to be achieved, it is advisable\u0000 to use the principal component method whose main task is to reduce the dimension of\u0000 multidimensional space with a minimal loss of information. The article describes the\u0000 basic conceptual approaches to the definition of principle components. Moreover, the\u0000 methodological principles of selecting the main components are presented. Among the many\u0000 ways to select principle components, the easiest way is selecting the first k-number of\u0000 components with the largest eigenvalues or to determine the percentage of the total\u0000 variance explained by each component. Many statistical data packages often use the\u0000 Kaiser method for this purpose. However, this method fails to take into account the fact\u0000 that when dealing with random data (noise), it is possible to identify components with\u0000 eigenvalues greater than one, or in other words, to select redundant components. We\u0000 conclude that when selecting the main components, the classical mechanisms should be\u0000 used with caution. The Parallel analysis method uses multiple data simulations to\u0000 overcome the problem of random errors. This method assumes that the components of real\u0000 data must have greater eigenvalues than the parallel components derived from simulated\u0000 data which have the same sample size and design, variance and number of variables. A\u0000 comparative analysis of the eigenvalues was performed by means of two methods: the\u0000 Kaiser criterion and the parallel Horn analysis on the example of several data sets. The\u0000 study shows that the method of parallel analysis produces more valid results with actual\u0000 data sets. We believe that the main advantage of Parallel analysis is its ability to\u0000 model the process of selecting the required number of main components by determining the\u0000 point at which they cannot be distinguished from those generated by simulated\u0000 noise.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43925774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new industrial strategy for Europe – new indicators of the results of its\u0000 implementation","authors":"Oleg Krekhivskyi, O. Salikhova","doi":"10.59170/stattrans-2023-012","DOIUrl":"https://doi.org/10.59170/stattrans-2023-012","url":null,"abstract":"This paper discusses the experiences resulting from EU’s adoption and\u0000 implementation of a wide variety of policy measures in response to the COVID-19 crisis.\u0000 These measures included stimulating the relocation and expansion of manufacturing to\u0000 reduce vulnerability, depending on imports, ensuring the stability and development of\u0000 industrial production. Using the example of the pharmaceutical industry in EU27 in the\u0000 years 1995–2018, the study proposes and tests a new approach to assessing the\u0000 consequences of relocation policies aimed at developing the local production potential,\u0000 increasing the value added by activity, and expanding the share of local value added in\u0000 industry exports. Specifically, the focus is on the formation of statistical analysis\u0000 tools for assessing the changes of the specialisation and identifying the country's\u0000 comparative advantages. The authors propose new indicators: RSP – coefficient of\u0000 Revealed Specialisation of Production, CAVA – coefficient of Comparative Advantage in\u0000 Value Added by Activity and EVA – coefficient of Comparative Advantages in the Domestic\u0000 Value Added Exports. Additionally, formulas for their calculation are provided which\u0000 allow the assessment of the position of Ukraine’s industries among a reference group,\u0000 widening the ‘revealed comparative advantage’ concept. Finally, a test of the new\u0000 methodology showed that it can be used to identify the comparative advantages of EU\u0000 member states supported by state assistance programmes involving the implementation of\u0000 business projects which aim to develop domestic production.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42128560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling methods for the concentration parameter and discrete baseline of the Dirichlet Process","authors":"Yang Liu, B. Nandram","doi":"10.2478/stattrans-2022-0040","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0040","url":null,"abstract":"Abstract There are many models in the current statistical literature for making inferences based on samples selected from a finite population. Parametric models may be problematic because statistical inference is sensitive to parametric assumptions. The Dirichlet process (DP) prior is very flexible and determines the complexity of the model. It is indexed by two hyper-parameters: the baseline distribution and concentration parameter. We address two distinct problems in the article. Firstly, we review the current sampling methods for the concentration parameter, which use the continuous baseline distribution. We compare three different methods: the adaptive rejection method, the mixture of Gammas method and the grid method. We also propose a new method based on the ratio of uniforms. Secondly, in practice, some survey responses are known to be discrete. If a continuous distribution is adopted as the baseline distribution, the model is misspecified and standard inference may be invalid. We propose a discrete baseline approach to the DP prior and sample the unobserved responses from the finite population both using a Polya urn scheme and a Multinomial distribution. We applied our discrete baseline approach to a Phytophthora data set.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"21 - 36"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49044383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of confidence intervals for variance components in an unbalanced one-way random effects model","authors":"Arisa Jiratampradab, Thidaporn Supapakorn, J. Suntornchost","doi":"10.2478/stattrans-2022-0047","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0047","url":null,"abstract":"Abstract The purpose of this paper is to study and compare the methods for constructing confidence intervals for variance components in an unbalanced one-way random effects model. The methods are based on a classical exact, generalised pivotal quantity, a fiducial inference and a fiducial generalised pivotal quantity. The comparison of criteria involves the empirical coverage probability that maintains at the nominal confidence level of 0.95 and the shortest average length of the confidence interval. The simulation results show that the method based on the generalised pivotal quantity and the fiducial inference perform very well in terms of both the empirical coverage probability and the average length of the confidence interval. The classical exact method performs well in some situations, while the fiducial generalised pivotal quantity performs well in a very unbalanced design. Therefore, the method based on the generalised pivotal quantity is recommended for all situations.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"149 - 160"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43459621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"k-th record estimator of the scale parameter of the α-stable distribution","authors":"M. Stachura, Barbara Wodecka","doi":"10.2478/stattrans-2022-0050","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0050","url":null,"abstract":"Abstract Various techniques of scale parameter estimation have been proposed in the case of alpha stable distributions. In the paper, the authors present an estimation technique that involves the k-th record theory. Although this theory is over 40 years old, its implementation in the classical extreme value theory – being the other cornerstone of the presented approach – is quite new, and tempting. Several theoretical properties of the introduced scale parameter estimators are presented. With the use of Monte Carlo methods, a comparative analysis is performed between the approach based on k-th records and approaches based on Hill’s and Pickands’ estimators. Additionally, the paper uses a real-life data set to illustrate how to effectively apply the k-th record estimator of the scale parameter. The research indicates several advantages of the k-th record approach over its other counterparts, especially when dealing with incomplete information about the underlying sample.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"203 - 215"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46703095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Missing data estimation based on the chaining technique in survey sampling","authors":"N. S. Thakur, D. Shukla","doi":"10.2478/stattrans-2022-0044","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0044","url":null,"abstract":"Abstract Sample surveys are often affected by missing observations and non-response caused by the respondents’ refusal or unwillingness to provide the requested information or due to their memory failure. In order to substitute the missing data, a procedure called imputation is applied, which uses the available data as a tool for the replacement of the missing values. Two auxiliary variables create a chain which is used to substitute the missing part of the sample. The aim of the paper is to present the application of the Chain-type factor estimator as a means of source imputation for the non-response units in an incomplete sample. The proposed strategies were found to be more efficient and bias-controllable than similar estimation procedures described in the relevant literature. These techniques could also be made nearly unbiased in relation to other selected parametric values. The findings are supported by a numerical study involving the use of a dataset, proving that the proposed techniques outperform other similar ones.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"91 - 111"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45663122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Weibull lifetime model with randomised failure-free time","authors":"P. Sulewski, Magdalena Szymkowiak","doi":"10.2478/stattrans-2022-0042","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0042","url":null,"abstract":"Abstract The paper shows that treating failure-free time in the three-parameter Weibull distribution not a constant, but as a random variable makes the resulting distribution much more flexible at the expense of only one additional parameter.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"59 - 76"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47418114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Zero-modified Poisson-Modification of Quasi Lindley distribution and its application","authors":"R. Tharshan, P. Wijekoon","doi":"10.2478/stattrans-2022-0045","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0045","url":null,"abstract":"Abstract The Poisson-Modification of Quasi Lindley (PMQL) distribution is a newly introduced mixed Poisson distribution for over-dispersed count data. The aim of this article is to introduce the Zero-modified PMQL (ZMPMQL) distribution as an alternative to the PMQL distribution in order to accommodate zero inflation/deflation. The method of obtaining the ZMPMQL distribution jointly with some of its important properties, namely the probability mass and distribution functions, mean, variance, index of dispersion, and quantile function are presented. Furthermore, some of its special cases are discussed. The maximum likelihood (ML) estimation method is used for the unknown parameter estimation. A simulation study is conducted in order to evaluate the asymptotic theory of the ML estimation method and to show the superiority of the ML method over the method of moments estimation. The applicability of the introduced distribution is illustrated by using a real-world data set.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"22 1","pages":"113 - 128"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41290520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Changes in the structure of household disposable income in selected countries as a reflection of crises after 2000","authors":"Richard Hindls, L. Marek, Stanislava Hronová","doi":"10.2478/stattrans-2022-0039","DOIUrl":"https://doi.org/10.2478/stattrans-2022-0039","url":null,"abstract":"Abstract Wages and salaries represent the most important component of household disposable income. The aim of the article is to examine how the relationship between the shares of households’ wages and final consumption expenditure in their gross disposable income has developed over the past 20 years. The presented analysis uses publicly available national accounts data for 30 countries for the period of 2000–2019. The studied indicators include the proportion of households’ wages and salaries, and final consumption expenditure in their gross disposable income. Using the proposed method based on the evaluation of changes in the spatial map, it is possible to observe any significant changes in these proportion values in the years of financial crisis and recession, as well as in the years of prosperity. The procedure can therefore serve as an indicator of appreciable changes in economic development.","PeriodicalId":37985,"journal":{"name":"Statistics in Transition","volume":"23 1","pages":"1 - 19"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43006856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}