Model logistickej regresie pre longitudinálne údaje

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dc.contributor.author Labudová, Viera
dc.contributor.author Lakatová, Martina
dc.date.accessioned 2019-01-03T14:06:39Z
dc.date.available 2019-01-03T14:06:39Z
dc.date.issued 2018
dc.identifier.issn 1211-555X (Print)
dc.identifier.issn 1804-8048 (Online)
dc.identifier.uri https://hdl.handle.net/10195/71993
dc.description.abstract The objective of this paper is to describe particularity of longitudinal data and methods which can be used to analyse them. The assumption of usual tools used for analysis is the independence of observations. In order to analyse of longitudinal data, we have to make provisions for their particularity, which is the dependence of observations. Therefore, while we analyse them, we must employ methods that are adjusted to that dependence. Several approaches have been proposed to model binary outcomes that arise from longitudinal studies. Most of the approaches can be grouped into two classes: the population-averaged and subject-specific approaches. The generalized estimating equations (GEE) method is used to estimate population averaged effects. In this paper, we investigate the Generalized Estimating Equation (GEE) capabilities of PROC GENMOD for correlated outcome data to fit models using unspecified (unstructured) correlation structure. The data from EU SILC was used to find out how material deprivation of households in the Slovak Republic (material deprivation: yes (1), no (0)) is linked to their available characteristics. en
dc.format p. 163 - 174
dc.language.iso sk
dc.publisher Univerzita Pardubice cze
dc.relation.ispartof Scientific papers of the University of Pardubice. Series D, Faculty of Economics and Administration. 44/2018 en
dc.rights open access en
dc.subject longitudinal data analysis en
dc.subject material deprivation en
dc.subject generalized estimating equation model en
dc.subject EU SILC
dc.title Model logistickej regresie pre longitudinálne údaje sk
dc.title.alternative Logistic regression model for longitudinal data en
dc.type Article en
dc.peerreviewed yes en
dc.publicationstatus published en
dc.subject.jel C10
dc.subject.jel M31
dc.subject.jel O10


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