Assessment of health and social security agency participants proportion using hierarchical bayesian small area estimation

2021 ◽  
Vol 16 (4) ◽  
pp. 241-250
Author(s):  
Ferra Yanuar ◽  
Atika Defita Sari ◽  
Dodi Devianto ◽  
Aidinil Zetra
Keyword(s):  
Social Security ◽  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Variable ◽  
Least Square ◽  
Hierarchical Bayesian ◽  
Strongly Correlated ◽  
Small Areas ◽  
Area Estimation ◽  
Owned Company

Data on the number of health insurance participants at the subdistrict level is crucial since it is strongly correlated with the availability of health service centers in the areas. This study’s primary purpose is to predict the proportion of health and social security participants of a state-owned company named Badan Penyelenggara Jaminan Sosial Kesehatan (BPJS) in eleven subdistricts in Padang, Indonesia. The direct, ordinary least square, and hierarchical Bayesian for small area estimation (HB-SAE) methods were employed in obtaining the best estimator for the BPJS participants in these small areas. This study found that the HB-SAE method resulted in better estimation than two other methods since it has the smallest standard deviation value. The auxiliary variable age (percentage of individuals more than 50 years old) and the percentage of health complaints have a significant effect on the proportion of the number of BPJS participants based on the HB-SAE method.

Hierarchical Bayesian small area estimation for circular data

2016 ◽  
Vol 44 (4) ◽  
pp. 416-430 ◽  
Author(s):  
Daniel Hernandez-Stumpfhauser ◽  
F. Jay Breidt ◽  
Jean D. Opsomer
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Circular Data ◽  
Hierarchical Bayesian ◽  
Area Estimation

scholarly journals MEMODELKAN TINGKAT PENGANGGURAN DI KOTA DENPASAR DENGAN PENDUGAAN AREA KECIL EMPIRICAL BAYES

2021 ◽  
Vol 10 (2) ◽  
pp. 81
Author(s):  
REYNALDO PANJI WICAKSONO ◽  
I KOMANG GDE SUKARSA ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Economic Development ◽  
Unemployment Rate ◽  
Small Area ◽  
Empirical Bayes ◽  
Small Area Estimation ◽  
Economic Conditions ◽  
Small Areas ◽  
Area Estimation ◽  
Direct Estimation ◽  
Selection Of

Economic development are described by the unemployment rate. The higher unemployment rate, the weaker economic conditions. Nowadays more policies require information on small areas. The direct estimation does not provide accurate results in smaller areas. Thus the small area estimation becomes an alternative to estimate the parameters. The accuracy depends on the selection of the predictors. In 2019, the unemployment rate in Denpasar is 2,22%. The result shows that the unemployment rate in each district in Denpasar varies from 0,1% to 0,3%

scholarly journals PENDUGAAN ANGKA KEMISKINAN DI KABUPATEN PADANG PARIAMAN MENGGUNAKAN SMALL AREA ESTIMATION DENGAN PENDEKATAN HIERARCHICAL BAYESIAN (HB) LOGLOGISTIK

2019 ◽  
Vol 8 (2) ◽  
pp. 76
Author(s):  
Jusri Repi Basri Yuliani ◽  
Maiyastri Maiyastri ◽  
Rita Diana
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Gibbs Sampling ◽  
Standard Error ◽  
Small Area ◽  
Small Area Estimation ◽  
Hierarchical Bayesian ◽  
Area Estimation ◽  
Level Model

Penelitian ini mengkaji tentang pendekatan Hierarchical Bayesian (HB) Loglogistik yang diaplikasikan pada Small Area Estimation (SAE) dengan tujuan mengestimasi tingkat kemiskinan di Kabupaten Padang Pariaman. Metode pendugaan area kecil yang digunakan pada penelitian ini adalah model level area dasar (basic area level model ) dengan bantuan variabel penyerta yang tersedia pada level kecamatan. Variabel penyerta yang digunakan pada penelitian ini yaitu rasio SLTA/Sederajat (X1), persentase keluarga pertanian (X2), rasio industri mikro kecil (X3), persentase buruh tani dalam setiap anggota keluarga (X4), kepadatan penduduk (X5), dan persentase penduduk pelanggan listrik PLN (X6). Bentuk integrasi yang kompleks dari sebaran peluang bersyarat pada model diselesaikan menggunakan Markov Chain Monte Carlo (MCMC) dengan menerapkan algortima Gibbs Sampling dan bantuan software WinBugs 1.4.3. Hasil estimasi menggunkan model HB yang diperoleh dibandingkan dengan hasil estimasi pendugaan langsung dengan memperhatikan nilai standard error sebagai tolok ukurnya. Hasil pendugaan tingkat kemiskinan untuk level kecamatan di Kabupaten Padang Pariaman dengan model HB menunjukkan nilai standard error yang kecil.Kata Kunci: Tingkat kemiskinan, Small Area Estimation, Hierarchical Bayesian

scholarly journals Spatiality in small area estimation: A new structure with a simulation study

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Yadollah Mehrabi ◽  
Amir Kavousi ◽  
Ahmad-Reza Baghestani ◽  
Mojtaba Soltani-Kermanshahi
Keyword(s):  
Spatial Correlation ◽  
Small Area ◽  
Small Area Estimation ◽  
Synthetic Data ◽  
Practical Applications ◽  
Small Areas ◽  
Area Estimation ◽  
Novel Structure ◽  
Specific Behaviour ◽  
Average Mean Square Error

In numerous practical applications, data from neighbouring small areas present spatial correlation. More recently, an extension of the Fay–Herriot model through the Simultaneously Auto- Rregressive (SAR) process has been considered. The Conditional Auto-Regressive (CAR) structure is also a popular choice. The reasons of using these structures are theoretical properties, computational advantages and relative ease of interpretation. However, the assumption of the non-singularity of matrix (Im-ρW) is a problem. We introduce here a novel structure of the covariance matrix when approaching spatiality in small area estimation (SAE) comparing that with the commonly used SAR process. As an example, we present synthetic data on grape production with spatial correlation for 274 municipalities in the region of Tuscany as base data simulating data at each area and comparing the results. The SAR process had the smallest Root Average Mean Square Error (RAMSE) for all conditions. The RAMSE also generally decreased with increasing sample size. In addition, the RAMSE valuess did not show a specific behaviour but only spatially correlation coefficient changes led to a stronger decrease of RAMSE values than the SAR model when our new structure was applied. The new approach presented here is more flexible than the SAR process without severe increasing RAMSE values.

Hierarchical Bayesian models for small area estimation of forest variables using LiDAR

2018 ◽  
Vol 204 ◽  
pp. 287-295 ◽  
Author(s):  
Neil R. Ver Planck ◽  
Andrew O. Finley ◽  
John A. Kershaw ◽  
Aaron R. Weiskittel ◽  
Megan C. Kress
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Bayesian Models ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Models ◽  
Area Estimation

ESTIMASI ANGKA PARTISIPASI KASAR PERGURUAN TINGGI PROVINSI PAPUA MELALUI SMALL AREA ESTIMATION

2020 ◽  
Vol 2019 (1) ◽  
pp. 104-109
Author(s):  
Budi Subandriyo
Keyword(s):  
Sustainable Development ◽  
Small Area ◽  
Sustainable Development Goals ◽  
Small Area Estimation ◽  
Auxiliary Variable ◽  
Best Linear Unbiased Predictor ◽  
Area Estimation ◽  
Direct Estimation ◽  
Development Goals ◽  
Best Linear Unbiased

Angka Partisipasi Kasar (APK) m0erupakan salah satu indikator statistik yang digunakan untuk melihat besarnya tingkat partisipasi pendidikan pada suatu wilayah. Besar atau kecilnya nilai APK perguruan tinggi menunjukkan seberapa mudah penduduk untuk mengakses Pendidikan di perguruan tinggi. Hal ini sesuai dengan tujuan pembangunan berkelanjutan (Sustainable Development Goals) yang memiliki program untuk terus meningkatkan kesempatan belajar, salah satunya pendidikan di perguruan tinggi. Oleh karena itu, diperlukan upaya peningkatan akses pendidikan di universitas dan perguruan tinggi melalui penyediaan data APK-PT yang akurat. Apabila dilihat berdasarkan daerah tingkat provinsi, Provinsi Papua merupakan provinsi dengan APK-PT dua terbawah di antara provinsi lainnya yaitu sebesar 19,03 persen. Akan tetapi, ketersediaan data APK-PT hingga tingkar kabupaten atau kota masih belum tersedia karena kurangnya ukuran sampel. Salah satu upaya untuk mengoptimalkan sampel yang tersedia dan menghasil estimasi APK-PT di tingkat kabupaten/kota yaitu dengan menggunakan metode Small Area Estimation (SAE) berbasis area level. Pada penelitian ini digunakan data Survei Sosial Ekonomi Nasional (SUSENAS) 2018 untuk memperoleh estimasi langsung (direct estimation) APK-PT dan Potensi Desa (PODES) 2018 di Provinsi Papua sebagai variabel penyerta (auxiliary variable) dalam pemodelan SAE. Metode SAE yang digunakan adalah Empirical Best Linear Unbiased Predictor – Fay Herriot (EBLUP-FH) dan EBLUP benchmarking seperti EBLUP Difference Benchmarking (EBLUP-DB), EBLUP You-Rao Benchmarking (EBLUP-YR), dan EBLUP Augmented Bencharking (EBLUP-AB). Berdasarkan hasil penelitian disimpulkan bahwa penggunaan estimasi SAE yang cocok pada data APK-PT di Provinsi Papua adalah model EBLUP Augmented Benchmarking dengan nilai rata-rata MSE terendah yaitu sebesar 22,06 persen.

A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables

2011 ◽  
Vol 41 (6) ◽  
pp. 1189-1201 ◽  
Author(s):  
Michael E. Goerndt ◽  
Vicente J. Monleon ◽  
Hailemariam Temesgen
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Estimation Methods ◽  
Stem Volume ◽  
Small Areas ◽  
Forest Density ◽  
Area Estimation ◽  
Forest Attributes ◽  
Areas Of Interest

One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were compared for estimating a variety of forest attributes for small areas using ground data and light detection and ranging (LiDAR) derived auxiliary information. The small areas of interest consisted of delineated stands within a larger forested population. Four different estimation methods were compared for predicting forest density (number of trees/ha), quadratic mean diameter (cm), basal area (m2/ha), top height (m), and cubic stem volume (m3/ha). The precision and bias of the estimation methods (synthetic prediction (SP), multiple linear regression based composite prediction (CP), empirical best linear unbiased prediction (EBLUP) via Fay–Herriot models, and most similar neighbor (MSN) imputation) are documented. For the indirect estimators, MSN was superior to SP in terms of both precision and bias for all attributes. For the composite estimators, EBLUP was generally superior to direct estimation (DE) and CP, with the exception of forest density.

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