Multivariate spatial hierarchical Bayesian empirical likelihood methods for small area estimation

Stat ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 108-116 ◽  
Author(s):  
Aaron T. Porter ◽  
Scott H. Holan ◽  
Christopher K. Wikle
Keyword(s):  
Empirical Likelihood ◽  
Small Area ◽  
Small Area Estimation ◽  
Hierarchical Bayesian ◽  
Likelihood Methods ◽  
Area Estimation

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 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

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 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

Empirical likelihood for small area estimation

Biometrika ◽  
2011 ◽  
Vol 98 (2) ◽  
pp. 473-480 ◽  
Author(s):  
S. Chaudhuri ◽  
M. Ghosh
Keyword(s):  
Empirical Likelihood ◽  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation

scholarly journals Hierarchical Bayesian Small Area Estimation Using Weakly Informative Priors in Ecologically Homogeneous Areas of the Interior Western Forests

2021 ◽  
Vol 4 ◽  
Author(s):  
Grayson W. White ◽  
Kelly S. McConville ◽  
Gretchen G. Moisen ◽  
Tracey S. Frescino
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Tree Canopy ◽  
Hierarchical Bayesian ◽  
Area Estimation ◽  
Tree Canopy Cover ◽  
Informative Prior Distribution ◽  
Area Variation ◽  
Forest Attributes ◽  
Response Variables

The U.S. Forest Inventory and Analysis Program (FIA) collects inventory data on and computes estimates for many forest attributes to monitor the status and trends of the nation's forests. Increasingly, FIA needs to produce estimates in small geographic and temporal regions. In this application, we implement area level hierarchical Bayesian (HB) small area estimators of several forest attributes for ecosubsections in the Interior West of the US. We use a remotely-sensed auxiliary variable, percent tree canopy cover, to predict response variables derived from ground-collected data such as basal area, biomass, tree count, and volume. We implement four area level HB estimators that borrow strength across ecological provinces and sections and consider prior information on the between-area variation of the response variables. We compare the performance of these HB estimators to the area level empirical best linear unbiased prediction (EBLUP) estimator and to the industry-standard post-stratified (PS) direct estimator. Results suggest that when borrowing strength to areas which are believed to be homogeneous (such as the ecosection level) and a weakly informative prior distribution is placed on the between-area variation parameter, we can reduce variance substantially compared the analogous EBLUP estimator and the PS estimator. Explorations of bias introduced with the HB estimators through comparison with the PS estimator indicates little to no addition of bias. These results illustrate the applicability and benefit of performing small area estimation of forest attributes in a HB framework, as they allow for more precise inference at the ecosubsection level.

Sae: A Stata Package For Unit Level Small Area Estimation

2018 ◽  
Author(s):  
Minh Cong Nguyen ◽  
Paul Corral ◽  
Joao Pedro Azevedo ◽  
Qinghua Zhao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Unit Level
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