Clusterwise multivariate regression of mixed-type panel data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10488935" target="_blank" >RIV/00216208:11320/24:10488935 - isvavai.cz</a>

Výsledek na webu

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=JF5mtEja9d" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=JF5mtEja9d</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11222-023-10304-5" target="_blank" >10.1007/s11222-023-10304-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Clusterwise multivariate regression of mixed-type panel data

Popis výsledku v původním jazyce

Multivariate panel data of mixed type are routinely collected in many different areas of application, often jointly with additional covariates which complicate the statistical analysis. Moreover, it is often of interest to identify unknown groups of subjects in a study population using such data structure, i.e., to perform clustering. In the Bayesian framework, we propose a finite mixture of multivariate generalised linear mixed effects regression models to cluster numeric, binary, ordinal and categorical panel outcomes jointly. The specification of suitable priors on the model parameters allows for convenient posterior inference based on Markov chain Monte Carlo (MCMC) sampling with data augmentation. This approach allows to classify subjects in the data and new subjects as well as to characterise the cluster-specific models. Model estimation and selection of the number of data clusters are simultaneously performed when approximating the posterior for a single model using MCMC sampling without resorting to multiple model estimations. The performance of the proposed methodology is evaluated in a simulation study. Its application is illustrated on two data sets, one from a longitudinal patient study to infer prognosis groups, and a second one from the Czech part of the EU-SILC survey where households are annually interviewed to obtain insights into changes in their financial capability.

Název v anglickém jazyce

Clusterwise multivariate regression of mixed-type panel data

Popis výsledku anglicky

Multivariate panel data of mixed type are routinely collected in many different areas of application, often jointly with additional covariates which complicate the statistical analysis. Moreover, it is often of interest to identify unknown groups of subjects in a study population using such data structure, i.e., to perform clustering. In the Bayesian framework, we propose a finite mixture of multivariate generalised linear mixed effects regression models to cluster numeric, binary, ordinal and categorical panel outcomes jointly. The specification of suitable priors on the model parameters allows for convenient posterior inference based on Markov chain Monte Carlo (MCMC) sampling with data augmentation. This approach allows to classify subjects in the data and new subjects as well as to characterise the cluster-specific models. Model estimation and selection of the number of data clusters are simultaneously performed when approximating the posterior for a single model using MCMC sampling without resorting to multiple model estimations. The performance of the proposed methodology is evaluated in a simulation study. Its application is illustrated on two data sets, one from a longitudinal patient study to infer prognosis groups, and a second one from the Czech part of the EU-SILC survey where households are annually interviewed to obtain insights into changes in their financial capability.

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/GA21-13323S" target="_blank" >GA21-13323S: Mikro předpovídání a změny režimu v ekonometrii - MiFReSE</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Statistics and Computing

ISSN

0960-3174

e-ISSN

1573-1375

Svazek periodika

34

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

001104143200001

EID výsledku v databázi Scopus

2-s2.0-85177661987