Count data are most commonly modeled using the Poisson model, or by one of its many extensions. In this study, a Poisson generalized linear mixed model (GLMM) with spatio-temporal random effects was modeled using two approaches. They are conditional autoregressive linear models and conditional autoregressive adaptive models. The models in this paper are fitted in a Bayesian setting using Markov chain Monte Carlo simulation. All parameters whose full conditional distributions have a closed-form distribution are Gibbs sampled, which includes the regression parameters and the random effects, as well as the variance parameters in all models. The models are applied to child labor in Sumatra between 2014 and 2017. Our main results show that the unemployment rate, illiteracy rates, and dropout rates influence the child labor number in Sumatra. Of the two models used, the spatio-temporal model with CAR adaptive is the best model by producing a simpler model but requires more time to build the model.
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22 December 2022
INTERNATIONAL CONFERENCE ON STATISTICS AND DATA SCIENCE 2021
22 September 2021
Bogor, Indonesia
Research Article|
December 22 2022
Spatio temporal random effect models for child labor mapping
Ita Wulandari;
Ita Wulandari
a)
1
Polytechnic of Statistics STIS
, Jl. Otto Iskandardinata No. 64C, Jakarta, Indonesia
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Anwar Fitrianto;
Anwar Fitrianto
b)
2
Statistics Department, Bogor Agricultural University
, Jl. Raya Dramaga Bogor, Bogor, Indonesia
b)Corresponding author: [email protected]
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Anik Djuraidah;
Anik Djuraidah
c)
2
Statistics Department, Bogor Agricultural University
, Jl. Raya Dramaga Bogor, Bogor, Indonesia
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I. Made Sumertajaya
I. Made Sumertajaya
d)
2
Statistics Department, Bogor Agricultural University
, Jl. Raya Dramaga Bogor, Bogor, Indonesia
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b)Corresponding author: [email protected]
AIP Conf. Proc. 2662, 020037 (2022)
Citation
Ita Wulandari, Anwar Fitrianto, Anik Djuraidah, I. Made Sumertajaya; Spatio temporal random effect models for child labor mapping. AIP Conf. Proc. 22 December 2022; 2662 (1): 020037. https://doi.org/10.1063/5.0108018
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