Comparison of methods for generalized linear mixed models parameters estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F17%3A00316350" target="_blank" >RIV/68407700:21340/17:00316350 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Comparison of methods for generalized linear mixed models parameters estimation
Original language description
During the last few years generalized linear mixed models have been increasingly used to solve a large variety of problems ranging from medical research to insurance. GLMMs are an extension of generalized linear models that contain both fixed and random effects (hence mixed models). After the idea of these models is presented, the logistic regression model, which is a member of this group of models that is used in small area estimation, is focused on. Two methods of parameter estimation are used, namely the EM algorithm and the PQL method, and they are compared by means of a simulation experiment. The properties of parameter estimates obtained by the respective methods is then studied with respect to increasing data contamination.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
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OECD FORD branch
50403 - Social topics (Women´s and gender studies; Social issues; Family studies; Social work)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
SPMS 2017 - Stochastic and Physical Monitoring Systems, Proceedings of the international conference
ISBN
978-80-01-06338-5
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
41-47
Publisher name
Česká technika - nakladatelství ČVUT
Place of publication
Praha
Event location
Dobřichovice
Event date
Jun 19, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000425554500006

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