Bayesian analysis of Turkish Income and Living Conditions data, using clustered longitudinal ordinal modelling with Bridge distributed random-effects

05/03/2019
by   Özgür Asar, et al.
0

This paper is motivated by the panel surveys, called Statistics on Income and Living Conditions (SILC), conducted annually on (randomly selected) country-representative households to monitor EU 2020 aims on poverty reduction. We particularly consider the surveys conducted in Turkey, within the scope of integration to the EU, between 2010 and 2013. Our main interests are on health aspects of economic and living conditions. The outcome is self-reported health that is clustered longitudinal ordinal, since repeated measures of it are nested within individuals and individuals are nested within families. Economic and living conditions were measured through a number of individual- and family-level explanatory variables. The questions of interest are on the marginal relationships between the outcome and covariates that are addressed using a polytomous logistic regression with Bridge distributed random-effects. This choice of distribution allows one to directly obtain marginal inferences in the presence of random-effects. Widely used Normal distribution is also considered as the random-effects distribution. Samples from the joint posterior density of parameters and random-effects are drawn using Markov Chain Monte Carlo. Interesting findings from public health point of view are that differences were found between sub-groups of employment status, income level and panel year in terms of odds of reporting better health.

READ FULL TEXT
research
11/08/2021

Bayesian modelling of statistical region- and family-level clustered ordinal outcome data from Turkey

This study is concerned with the analysis of three-level ordinal outcome...
research
06/01/2018

Bayesian Logistic Regression for Small Areas with Numerous Households

We analyze binary data, available for a relatively large number (big dat...
research
09/08/2022

Bayes factors for longitudinal model assessment via power posteriors

Bayes factor, defined as the ratio of the marginal likelihood functions ...
research
10/21/2019

Marginally Interpretable Linear Transformation Models for Clustered Observations

Clustered observations are ubiquitous in controlled and observational st...
research
01/15/2018

Latent nested nonparametric priors

Discrete random structures are important tools in Bayesian nonparametric...
research
06/11/2019

A mnotone data augmentation algorithm for longitudinal data analysis via multivariate skew-t, skew-normal or t distributions

The mixed effects model for repeated measures (MMRM) has been widely use...

Please sign up or login with your details

Forgot password? Click here to reset