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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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