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Conditional dependence of long period time series of numbers of deaths by individual causes
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SYSNO ASEP 0450187 Document Type E - Electronic Document R&D Document Type The record was not marked in the RIV Title Conditional dependence of long period time series of numbers of deaths by individual causes Author(s) Podolská, Kateřina (UFA-U) RID, ORCID Number of authors 1 Issue data Workshop of the EAPS Health, Morbidity and Mortality Working Group, Prague, University of Economics,, 16.–18. September 2015 Publication form Online - E Language eng - English Country CZ - Czech Republic Keywords diseases of the nervous systém ; mortality by causes ; graphical models of conditional independences ; solar aktivity Subject RIV DG - Athmosphere Sciences, Meteorology Institutional support UFA-U - RVO:68378289 Annotation Graphical models of conditional independences (CIG) are an important instrument of the multivariate statistics. They describe and transparently represent the structure of dependence relationships in a given set of random vectors. The principal aim of this paper is exploring the possibilities of application data analysis by graphical models to thelong period time series of daily aggregated numbers of deaths byindividual causes of death and the database of physical parameters of the ionosphere-inner magnetosphere region which can influence human organism. In particular, we focused on the causes of death according to ICD-10 of groups VI.Diseases of the nervous system and IX.Diseases of the circulatory system. We used time series of daily aggregated numbers of deaths separately for both sexes at the age groups under 39 and over 40+. This method appears to be useful for studying this correlationships and can be applied even in the case when classical parametric methods are not convenient, e.g. for non-continuous time series etc. We consider the structure of pairwise dependence of its individual components, looking for the maximum likelihood estimate of the variance matrix under conditions given by the graphical model. The CIG method allowed us to implement additional time series variables into model and the data best fit model is computed. We employ CIG multivariate statistic methods applied also to long period daily observational data for find out intragroup relationships between daily aggregated numbers of deaths by individual causes of death in numerous groups of diagnoses according to ICD-10. Workplace Institute of Atmospheric Physics Contact Kateřina Adamovičová, adamovicova@ufa.cas.cz, Tel.: 272 016 012 ; Kateřina Potužníková, kaca@ufa.cas.cz, Tel.: 272 016 019 Year of Publishing 2016 Electronic address http://hmmwg2015.vse.cz/wp-content/uploads/2014/09/Podolska_2015.pdf
Number of the records: 1