Estimasi Tingkat Kemiskinan Anak Level Kabupaten/Kota di Provinsi Banten Tahun 2018-2021 dengan Small Area Estimation (SAE) Rao-Yu Pendekatan Hierarchical Bayes - Salis, Dian Rahmawati; Ubaidillah, Azka (Politeknik Statistika STIS, 2023)
ABSTRACT Child poverty have a significant impact on the future quality of the adult population. Estimating the level of child poverty accurately, especially at regency/city level, is crucial for targeted policy interventions. Direct estimations based on SUSENAS data have a Relative Standard Error (RSE) value of more than 25%, necessitating the use of an indirect method called small area estimation (SAE). The province of Banten has consistently had the lowest Gross Participation Rate (GPR) for Early Childhood Education (ECE) among the provinces in Java over the years which can be interpreted as an early indication of limited access to children's education due to poverty. In this study, the level of child poverty in the districts/cities of Banten Province was estimated using the hierarchical bayes Rao-Yu model with normal and beta distribution approach. The results of this study indicate that although it produces the best precision, the SAE Rao-Yu HB Beta estimation has results with a smaller level of consistency than the normal SAE Rao-Yu HB estimation.