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Title Využitie modelov so zmiešanými efektmi a metódy RE‐EM stromu pri predikcii finančnej tiesne Par.title Utilization of mixed effects models and RE-EM tree method in financial distress prediction Author info Lukáš Sobíšek, Karel Helman, Mária Stachová Author Sobíšek Lukáš (25%)
Co-authors Helman Karel (5%)
Stachová Mária 1981- (70%) UMBEF05 - Katedra kvantitatívnych metód a informačných systémov
Source document Forum Statisticum Slovacum : vedecký recenzovaný časopis Slovenskej štatistickej a demografickej spoločnosti. Roč. 13, č. 2 (2017), s. 48-56. - Bratislava : Slovenská štatistická a demografická spoločnosť, 2017 Keywords generalized regression mixed effect model financial health of corporations RE-EM tree model Language English Country Slovak Republic systematics 33 Public work category ADF No. of Archival Copy 41428 Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ 
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Title Financial distress criteria defined by model based clustering Author info Mária Stachová ... [et al.] Author Stachová Mária 1981- (70%) UMBEF05 - Katedra kvantitatívnych metód a informačných systémov
Co-authors Sobíšek Lukáš (10%)
Gerthofer Michal (10%)
Helman Karel (10%)
Source document Conference proceedings : the 11th international days of statistics and economics, September 14-16, 2017, Prague. online, pp.1511-1520. - Praha : Libuše Macáková - Melandrium, 2017 ; International days of statistics and economics konferencia Keywords financie - finance rizikové faktory - risk factors poistné produkty - insurance products poistenie - insurance Language English Country Czech Republic systematics 336 Annotation One of the important steps in financial distress analyses is to correctly and reasonably mark a company whether is, or it is not in financial distress risk. There are many definitions used in the past. Most of them are based on time static point of view and thus use only one year data. In this paper, we continue with our previous work that examined possibilities of the companies clustering in order to identify homogeneous clusters regarding to their financial distress by using micropanel data. Financial distress can be described as a situation when a company cannot pay or has a difficulty to pay off its financial obligations. In our analysis we consider three criteria to define this situation: the equity, the earnings after taxes and the current ratio value. These financial indicators data were collected over a few consecutive years and thus create a longitudinal data set. We compare a model based partitioning and k-means partitioning to cluster the time trajectories of these three cri URL https://msed.vse.cz/msed_2017/sbornik/front.html Public work category AFC No. of Archival Copy 42042 Catal.org. BB301 - Univerzitná knižnica Univerzity Mateja Bela v Banskej Bystrici Database xpca - PUBLIKAČNÁ ČINNOSŤ unrecognised