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BAYESIAN APPROACH TO THE PROCESS OF IDENTIFICATION OF THE DETERMINANTS OF INNOVATIVENESS

100%
Czyżewska M., Mroczek T.
Finansowy Kwartalnik Internetowy e-Finanse
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2014
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vol. 10
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issue 2
44-56
EN
Bayesian belief networks are applied in determining the most important factors of the innovativeness level of national economies. The paper is divided into two parts. The first presentsthe basic theory of Bayesian networks whereas in the second, the belief networks have been generated by an inhouse developed computer system called BeliefSEEKER which was implemented to generate the determinants influencing the innovativeness level of national economies.Qualitative analysis of the generated belief networks provided a way to define a set of the most important dimensions influencing the innovativeness level of economies and then the indicators that form these dimensions. It has been proven that Bayesian networks are very effective methods for multidimensional analysis and forming conclusions and recommendations regarding the strength of each innovative determinant influencing the overall performance of a country’s economy.
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The Effect of CAP Subsidies on the Technical Efficiency of Polish Dairy Farms

88%
Marzec J., Pisulewski A.
Central European Journal of Economic Modelling and Econometrics
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2017
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issue 3
3
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Modelling the occupational and educational choices of young people in Poland using Bayesian multinomial logit models

88%
Grzenda W.
Statistics in Transition new series
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2021
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vol. 22
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issue 3
175-191
EN
Binomial logit models are commonly used in the analysis of the situation of respondents on the labour market. Consequently, in most cases researchers consider two states: of being unemployed and employed or economically inactive and active. This paper focuses on the situation of young people aged 18 to 29 on the labour market in Poland. A major part of the people who comprise the studied group are still in education or combine education with work. Therefore, the participants of the research were divided into the following groups: the employed and not learning, those combining education with work, the unemployed, learners/students only, and those economically inactive and not at school. The model allowing an analysis which includes both the most common division into working and nonworking persons as well as the division proposed in this study is a nested logit model. This model has a hierarchical structure and is a special case of a multinomial logit model. In this paper, all models were estimated within the Bayesian approach. The findings show that continuing education by young people may result from their problems with finding a job; moreover, combining work with education is not the preferred form of professional activity. In addition, the study examines the inequalities observed on the Polish labour market.
4
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Comparing particulate matter dispersion in Thailand using the Bayesian Confidence Intervals for ratio of coefficients of variation

88%
Thangjai W., Niwitpong S.
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2020
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vol. 21
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issue 5
41-60
XX
Recently, harmful levels of air pollution have been detected in many provinces of Thailand. Particulate matter (PM) contains microscopic solids or liquid droplets that are so small that they can be inhaled and cause serious health problems. A high dispersion of PM is measured by a coefficient of variation of log-normal distribution. Since the log-normal distribution is often used to analyse environmental data such as hazardous dust particle levels and daily rainfall data. These data focus the statistical inference on the coefficient of variation. In this paper, we develop confidence interval estimation for the ratio of coefficients of variation of two log-normal distributions constructed using the Bayesian approach. These confidence intervals were then compared with the existing approaches: method of variance estimates recovery (MOVER), modified MOVER, and approximate fiducial approaches using their coverage probabilities and average lengths via Monte Carlo simulation. The simulation results show that the Bayesian confidence interval performed better than the others in terms of coverage probability and average length. The proposed approach and the existing approaches are illustrated using examples from data set PM10 level and PM2.5 level in the northern Thailand.
5
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Bayesian SVLEDEJ Model for Detecting Jumps in Logarithmic Growth Rates of One Month Forward Gas Contract Prices

75%
Kostrzewski M.
Central European Journal of Economic Modelling and Econometrics
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2016
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issue 3
6
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Evaluation of the financial condition of companies after the announcement of arrangement bankruptcy: application of the classical and Bayesian logistic regression

75%
Pawełek B., Kostrzewska J., Kostrzewski M., Gałuszka K.
Przegląd Statystyczny
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2020
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vol. 67
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issue 1
5-32
EN
The aim of this paper is to present the results of an assessment of the financial condition of companies from the construction industry after the announcement of arrangement bankruptcy, in comparison to the condition of healthy companies. The logistic regression model estimated by means of the maximum likelihood method and the Bayesian approach were used. The first achievement of our study is the assessment of the financial condition of companies from the construction industry after the announcement of bankruptcy. The second achievement is the application of an approach combining the classical and Bayesian logistic regression models to assess the financial condition of companies in the years following the declaration of bankruptcy, and the presentation of the benefits of such a combination. The analysis described in the paper, carried out in most part by means of the ML logistic regression model, was supplemented with information yielded by the application of the Bayesian approach. In particular, the analysis of the shape of the posterior distribution of the repeat bankruptcy probability makes it possible, in some cases, to observe that the financial condition of a company is not clear, despite clear assessments made on the basis of the point estimations.
7
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UZASADNIENIE I PRZEKONANIE JAKO PODSTAWY DOWODU W PROCESIE KARNYM

63%
Konieczny J.
Roczniki Administracji i Prawa
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2019
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vol. specjalny
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issue XIX
123-137
PL
Celem artykułu jest przedstawienie roli uzasadniania i przekonania w toku dowodzenia winy w procesie karnym. Punktem wyjścia jest wskazanie indukcyjnego charakteru rozumowań dowodowych i uznawanie ich konkluzji na podstawie decyzji organu procesowego. Decyzje te pojawiają się po osiągnięciu przez organ procesowy poziomu aspiracji do ich podjęcia; drugą podstawą może być ich oczekiwana użyteczność. Wymagania, dotyczące udowodnienia, zestawione zostały z koncepcją wiedzy. Jeśli założyć, że przypisanie określonemu podmiotowi wiedzy polega na posiadaniu przez ten podmiot uzasadnionego, trafnego przekonania, wtedy można przyjąć, że posiadanie takiej wiedzy jest równoznaczne z udowodnieniem w sensie procesowym. Narzędziami wspomagającymi dążenie do poprawności dowodzenia są funkcja przekonania w ujęciu Shafera-Dempstera oraz podejście bayesowskie w podejmowaniu decyzji dotyczących ustaleń faktycznych.
EN
The aim of the article is to present the role of justification and belief in the course of proving guilt in a criminal trial. The starting point is the indication of the inductive character of evidentiary reasoning and the acceptance of its conclusions on the basis of the decision making by trial authority. These decisions appear after the process in which this authority reaches the level of aspirations to make them; the second basis may be their expected usefulness. The requirements for proof are contrasted with the concept of knowledge. If one assumes that the attribution of knowledge to a particular subject consists in the possession of a justified, accurate belief by that subject, then one can assume that the possession of such knowledge is tantamount to proving in a trial sense. The tools supporting the pursuit of correctness of command are the Shafer-Dempster belief function and the Bayesian approach in making decisions about factual findings
8
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Pomiar efektywności zróżnicowanych technologicznie gospodarstw rolnych w Unii Europejskiej

63%
Marzec J., Pisulewski A.
Gospodarka Narodowa. The Polish Journal of Economics
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2020
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vol. 303
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issue 3
111-137
EN
The aim of the present study was to derive the characteristics of the production process for crop farms in the European Union member states. The paper uses regional data on farms taken from the Farm Accountancy Data Network (FADN). Therefore, the models that account for heterogeneity among the analysed regions, were used in the present study. In particular, the paper considers two approaches to modelling heterogeneity: deterministic and stochastic. The deterministic approach is reflected in the paper with the usage of translog production function model, which allows output elasticities to depend on the input levels. The stochastic approach is represented by a stochastic frontier model with random coefficients. The application of the above-mentioned concept allowed to derive the Cobb-Douglas (C–D) production function model with individual parameters. The parameters of the four models were estimated using the Bayesian approach. The obtained results indicate that the C–D model is the best. In addition, it was observed that for the EU average, the highest production elasticity is with respect to materials, while the lowest w.r.t area. Surprisingly, the results suggest a high mean technical efficiency of the analysed regions (0.95), with very small dispersion of these scores.
PL
Celem niniejszego opracowania jest określenie charakterystyk procesu produkcyjnego gospodarstw rolnych specjalizujących się w uprawach polowych w państwach członkowskich Unii Europejskiej. W pracy wykorzystano dane regionalne FADN. W związku z występującym zróżnicowaniem między regionami w pracy wykorzystano modele uwzględniające tę heterogeniczność. W szczególności rozważono dwa sposoby modelowania heterogeniczności: deterministyczny oraz stochastyczny. Odzwierciedleniem pierwszego sposobu jest wykorzystanie w niniejszej pracy modelu funkcji produkcji typu translog, który pozwala, żeby elastyczności produkcji względem nakładów czynników produkcji zależały od wielkości nakładów. Natomiast stochastyczny sposób modelowania heterogeniczności reprezentuje stochastyczny model graniczny z losowymi parametrami. Zastosowanie powyższej koncepcji pozwoliło na zbudowanie modelu funkcji produkcji typu Cobba i Douglasa (C–D) z indywidualnymi parametrami. Estymacji parametrów czterech modeli dokonano za pomocą podejścia bayesowskiego. Otrzymane wyniki jednoznacznie wskazują, że najlepszym modelem okazał się model C–D z indywidualnymi parametrami. Ponadto zaobserwowano, że dla średniej unijnej najwyższa elastyczność produkcji występuje względem nakładów materiałów, a najniższa względem areału. Natomiast dosyć zaskakującym wynikiem jest wysoki poziom średniej efektywności technicznej (0,95) przy bardzo niewielkim rozproszeniu tych ocen.
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