Clusterwise multivariate regression of mixed-type panel data
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Clusterwise multivariate regression of mixed-type panel data
Original language description
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.
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/GA21-13323S" target="_blank" >GA21-13323S: Micro Forecasting and Regime Switching in Econometrics - MiFReSE</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Statistics and Computing
ISSN
0960-3174
e-ISSN
1573-1375
Volume of the periodical
34
Issue of the periodical within the volume
1
Country of publishing house
GB - UNITED KINGDOM
Number of pages
20
Pages from-to
1-20
UT code for WoS article
001104143200001
EID of the result in the Scopus database
2-s2.0-85177661987