Classification based on multivariate mixed type longitudinal data with an application to the EU-SILC database

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Classification based on multivariate mixed type longitudinal data with an application to the EU-SILC database

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Classification based on multivariate mixed type longitudinal data with an application to the EU-SILC database

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA19-00015S" target="_blank" >GA19-00015S: Identifikace schémat časového vývoje indikátorů chudoby a sociálního vyčlenění domácností založená na vícerozměrných panelových datech smíšeného typu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Advances in Data Analysis and Classification

ISSN

1862-5347

e-ISSN

1862-5355

Svazek periodika

17

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

38

Strana od-do

369-406

Kód UT WoS článku

000815562600001

EID výsledku v databázi Scopus

2-s2.0-85132842665