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EN
Classification methods are recognised as useful tool for bankruptcy prediction. Among them the most popular are: linear discriminant function, Logit model, neural network and classification tree. The ideas and basic formulas of these methods are presented in the paper. Some examples of application those procedures, which were published in world and Polish literature, are mentioned in the following parts of the paper. Some effectiveness conditions of presented methods are discussed. In the conclusion it has been stressed, that the precision of bankruptcy prediction not strictly depend on classification method which has been used. Sources of errors in bankruptcy prediction has been discussed on the end of the paper. Among them important are: valuated character of financial ratios, as an impute variables in such models, problems in samples selection, which usually hasn't random character and unstable character of considered populations. Probability of firms' collapse strongly depends on the stage of business cycle.
EN
In this paper, we present the ranking of living standards in Poland in a cross-section of counties as constructed by using selected methods of linear ordering. The analysis was of a dynamic character and the study involved data from the years 2003 to 2012. The positions of the counties in the annual section rankings were distinguished. Additionally, we assessed the trends of the synthetic measure of the standard of living for each country between 2003-2012. An important issue when creating rankings was determining the spatial extent of the study. For example, when examining the standard of living in the counties of the Podkarpackie province, should not the counties in other provinces be taken into account as a reference point? In the case of multivariate analysis, the interaction of objects can indeed be changed by an extended context analysis. Therefore, in this paper, comparisons made of the rankings compliance obtained in the analysis were narrowed down to a single province with the results of the analyses being carried out in the broader context of all the counties in Poland. As with today’s data processing, it is not important whether the population of a few dozen or a few hundred objects is analysed. The conclusion from the conducted considerations is that one should always strive for the widest possible context for the research. We then propose a modification of the linear ordering method that takes the spatial relationships between the districts into account. Two rankings were presented: one where the neighbourhood matrix between the counties was applied and one where the length of the shared border was considered. In the discussion of the results, we highlighted the fact that spatial relationships should be determined separately for each diagnostic variable. In this way, the directions for further research were determined.
PL
W pracy przedstawiono ranking poziomu życia mieszkańców Polski w przekroju powiatów, skonstruowany za pomocą wybranych metod porządkowania liniowego. Analiza miała charakter dynamiczny, badaniem objęto dane z lat 2003-2012. Wyodrębniono pozycje powiatów w rankingach przekrojowych (dla poszczególnych lat), oceniono także kierunki zmian syntetycznej miary poziomu życia w latach 2003-2012 dla każdego powiatu. Ważną kwestią podczas tworzenia rankingów jest określenie zakresu przestrzennego prowadzonych badań. Przykładowo, czy badając poziom życia w powiatach województwa podkarpackiego nie należałoby, jako punktu odniesienia, przyjąć także powiatów z innych województw? Wszak w przypadku analizy wielowymiarowej wzajemne relacje obiektów mogą się zmieniać przy rozszerzonym kontekście analizy. Dlatego też w pracy dokonano porównań zgodności rankingów uzyskanych przy analizie zawężonej tylko do jednego województwa z wynikami analiz prowadzonych w szerszym kontekście – wszystkich powiatów w Polsce. Następnie zaproponowano modyfikację metody porządkowania liniowego, w której uwzględnione zostały przestrzenne relacje pomiędzy powiatami. W omówieniu uzyskanych wyników zwrócono uwagę na fakt, iż relacje przestrzenne powinny być określane odrębnie dla każdej zmiennej diagnostycznej.  W ten sposób określono kierunki dalszych badań.
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