Classification based on multivariate mixed type longitudinal data with an application to the EU-SILC database
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10451928" target="_blank" >RIV/00216208:11320/23:10451928 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Iq8wUpyqGP" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=Iq8wUpyqGP</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11634-022-00504-8" target="_blank" >10.1007/s11634-022-00504-8</a>
Alternative languages
Result language
angličtina
Original language name
Classification based on multivariate mixed type longitudinal data with an application to the EU-SILC database
Original language description
Although many present day studies gather data of a diverse nature (numeric quantities, binary indicators or ordered categories) on the same units repeatedly over time, there only exist limited number of approaches in the literature to analyse so-called mixed-type longitudinal data. We present a statistical model capable of joint modelling several mixed-type outcomes, which also accounts for possible dependencies among the investigated outcomes. A thresholding approach to link binary or ordinal variables to their latent numeric counterparts allows us to jointly model all, including latent, numeric outcomes using a multivariate version of the linear mixed-effects model. We avoid the independence assumption over outcomes by relaxing the variance matrix of random effects to a completely general positive definite matrix. Moreover, we follow model-based clustering methodology to create a mixture of such models to model heterogeneity in the temporal evolution of the considered outcomes. The estimation of such an hierarchical model is approached by Bayesian principles with the use of Markov chain Monte Carlo methods. After a successful simulation study with the aim to examine the ability to consistently estimate the true parameter values and thus discover the different patterns, the EU-SILC dataset consisting of Czech households that were followed for 4 years in a time span from 2005 to 2016 was analysed. The households were classified into groups with a similar evolution of several closely related indicators of monetary poverty based on estimated classification probabilities.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GA19-00015S" target="_blank" >GA19-00015S: Identification of Poverty and Social Exclusion Temporal Patterns of Households Based on Multivariate Mixed Type Panel Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Advances in Data Analysis and Classification
ISSN
1862-5347
e-ISSN
1862-5355
Volume of the periodical
17
Issue of the periodical within the volume
12
Country of publishing house
DE - GERMANY
Number of pages
38
Pages from-to
369-406
UT code for WoS article
000815562600001
EID of the result in the Scopus database
2-s2.0-85132842665