scholarly journals Evaluating the Approaches of Small Area Estimation Using Poverty Mapping Data

2021 ◽  
pp. 1-21
Author(s):  
Mizanur Rahman ◽  
Deluar J. Moloy ◽  
Sifat Ar Salan
Keyword(s):  
Small Area ◽  
Empirical Bayes ◽  
Small Area Estimation ◽  
Small Sample ◽  
Estimation Methods ◽  
Data Set ◽  
Area Estimation ◽  
Direct Estimation ◽  
Bivariate Correlation ◽  
Poverty Mapping

Nowadays, estimation demand in statistics is increased worldwide to seek out an estimate, or approximation, which may be a value which will be used for various purpose, albeit the input data could also be incomplete, uncertain, or unstable. The development of different estimation methods is trying to provide most accurate estimate and estimation theory deals with finding estimates with good properties. The demand of small area estimation (SAE) method has been increasing rapidly around the world because of its reliability compared to the traditional direct estimation methods, especially in the case of small sample size. This paper mainly focuses on the comparison of several indirect small area estimation methods (poststratified synthetic, SSD and EB estimates) with traditional direct estimator based on a renowned data set. Direct estimator is approximately unbiased but SSD and Post-stratified synthetic estimator is extreme biased. To cope up the problem, we conduct another model-based estimation procedure namely Empirical Bayes (EB) estimator, which is unbiased and compare them using their coefficient of variation (CV). To check the model assumption, we used Q-Q plot as well as a Histogram to confirm the normality, bivariate correlation, Akaike information criterion (AIC). JEL classification numbers: C13, C51, C51. Keywords: Small Area Estimation, Direct Estimation, Indirect Estimation, Empirical Bayes Estimator, Poverty Mapping.

scholarly journals SMALL AREA ESTIMATION METHOD WITH EMPIRICAL BAYES BASED ON BETA BINOMIAL MODEL IN GENERATED DATA

2020 ◽  
Vol 14 (1) ◽  
pp. 1-9
Author(s):  
Ferra Yanuar ◽  
Rahmatika Fajriyah ◽  
Dodi Devianto
Keyword(s):  
Binary Data ◽  
Small Area ◽  
Empirical Bayes ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Estimation Methods ◽  
Auxiliary Variables ◽  
Indirect Estimation ◽  
Area Estimation ◽  
Direct Estimation

Small Area Estimation is one of the methods that can be used to estimate parameters in an area that has a small population. This study aims to estimate the value of the binary data parameter using the direct estimation method and an indirect estimation method by using the Empirical Bayes approach. To illustrate the method, we consider three conditions: direct estimator, empirical Bayes (EB) with auxiliary variables, and empirical Bayes without auxiliary variables. The smaller value of Mean Square Error is used to determine the better method. The results showed that the indirect estimation methods (EB method) gave the parameter value that was not much different from the direct estimation value. Then, the MSE values of indirect estimation with an auxiliary variable are smaller than the direct estimation method.

scholarly journals Simple House Needs in Jember with Robust Small Area Estimation

2017 ◽  
Vol 18 (1) ◽  
pp. 1
Author(s):  
Frida Murtinasari ◽  
Alfian Futuhul Hadi ◽  
Dian Anggraeni
Keyword(s):  
Small Area ◽  
Robust Regression ◽  
Small Sample Size ◽  
Small Area Estimation ◽  
Small Sample ◽  
Estimation Methods ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Best Linear Unbiased ◽  
M Estimation

SAE (Small Area Estimation) is often used by researchers, especially statisticians to estimate parameters of a subpopulation which has a small sample size. Empirical Best Linear Unbiased Prediction (EBLUP) is one of the indirect estimation methods in Small Area Estimation. The presence of outliers in the data can not guarantee that these methods yield precise predictions . Robust regression is one approach that is used in the model Small Area Estimation. Robust approach in estimating such a small area known as the Robust Small Area Estimation. Robust Small Area Estimation divided into several approaches. It calls Maximum Likelihood and M- Estimation. From the result, Robust Small Area Estimation with M-Estimation has the smallest RMSE than others. The value is 1473.7 (with outliers) and 1279.6 (without outlier). In addition the research also indicated that REBLUP with M-Estimation more robust to outliers. It causes the RMSE value with EBLUP has five times to be large with only one outlier are included in the data analysis. As for the REBLUP method is relatively more stable RMSE results.

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 A COMPARISON OF SMALL AREA ESTIMATION METHODS FOR POVERTY MAPPING

2016 ◽  
Vol 17 (1) ◽  
pp. 41-66 ◽  
Author(s):  
María Guadarrama ◽  
Isabel Molina ◽  
J. N. K. Rao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Estimation Methods ◽  
Area Estimation ◽  
Poverty Mapping

Applications of Small Area Estimation to Generalization With Subclassification by Propensity Scores

2017 ◽  
Vol 43 (2) ◽  
pp. 182-224
Author(s):  
Wendy Chan
Keyword(s):  
Propensity Score ◽  
Small Area ◽  
Propensity Scores ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Population Data ◽  
Small Sample ◽  
Estimation Methods ◽  
Sample Sizes ◽  
Area Estimation

Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score–based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods such as subclassification by propensity score, where limited sample sizes lead to sparse strata. This article explores the potential of small area estimation methods to improve the precision of estimators in sparse strata using population data as a source of auxiliary information to borrow strength. Results from simulation studies identify the conditions under which small area estimators outperform conventional estimators and the limitations of this application to causal generalization studies.

scholarly journals Small Area Estimation of Smoke-free Workplace Polices and Home Rules in U.S. Counties

2021 ◽  
Author(s):  
Benmei Liu ◽  
Isaac Dompreh ◽  
Anne M Hartman
Keyword(s):  
Tobacco Control ◽  
Small Area ◽  
Small Sample Size ◽  
Health Hazard ◽  
Small Area Estimation ◽  
Small Sample ◽  
County Level ◽  
Area Estimation ◽  
Model Based ◽  
Workplace Policies

Abstract Background The workplace and home are sources of exposure to secondhand smoke (SHS), a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from SHS and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small area estimation techniques are needed to produce such estimates. Methods Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods. Results Model-based estimates of smoke-free workplace policies and home rules were produced for 3,134 (out of 3,143) U.S. counties. In 2014-2015, nearly 80% of U.S. adult workers were covered by smoke-free workplace policies, and more than 85% of U.S. adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules. Conclusions The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention. Implications Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, e.g., to other behavioral, policy, or health related topics.

scholarly journals District level child nutrition status in Bangladesh: An application of small area estimation method

2019 ◽  
Vol 53 (1) ◽  
pp. 45-61
Author(s):  
Mossamet Kamrun Nesa
Keyword(s):  
Small Area ◽  
Child Nutrition ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Nutrition Status ◽  
Estimation Methods ◽  
Small Scale ◽  
Demographic Health Survey ◽  
Area Estimation ◽  
National Surveys

National level indicators of child undernutrition often hide the real scenario across a country. In order to construct a child nutrition map, accurate estimates of undernutrition are required at very small spatial scales, typically the administrative units of a country or a region within a country. Although comprehensive data on child nutrition are collected in national surveys, the small scale estimates cannot be calculated using the standard estimation methods employed in national surveys, since such methods are designed to produce national or regional level estimates, and assume large samples. Small area estimation method has been widely used to find such micro-level estimates. Due to lack of unit level data, area level small area estimation methods (e.g., Fay-Herriot method) are widely used to calculate small-scale estimates. In Bangladesh, a few works have been done to estimate district level child nutrition status. The Bangladesh Demographic Health Survey covers all districts but district wise sample sizes are very small to get consistent estimates. In this paper, Fay-Herriot Model has been developed to calculate district wise estimates with efficient mean squared error. The Bangladesh Demographic Health Survey 2011 and Population Census 2011 are utilized for this study.

scholarly journals Poisson gamma model in empirical Bayes of small area estimation (SAE)

2019 ◽  
Vol 1317 ◽  
pp. 012006
Author(s):  
Ferra Yanuar ◽  
Nadya Cindy Eka Putri ◽  
Hazmira Yozza
Keyword(s):  
Small Area ◽  
Empirical Bayes ◽  
Small Area Estimation ◽  
Gamma Model ◽  
Area Estimation

scholarly journals SMALL AREA ESTIMATION DENGAN PENDEKATAN EMPIRICAL BAYES BERBASIS MODEL BETA-BINOMIAL UNTUK MENDUGA ANGKA PENGANGGURAN DI SUMATERA BARAT

2019 ◽  
Vol 8 (1) ◽  
pp. 120
Author(s):  
Nurmaylina Zaja ◽  
Hazmira Yozza ◽  
Ferra Yanuar
Keyword(s):  
Small Area ◽  
Empirical Bayes ◽  
Small Area Estimation ◽  
Area Estimation

Penelitian ini bertujuan untuk menduga angka pengangguran di kabupaten/kota di Sumatea Barat dengan metode Small Area Estimation dengan pendekatan Empirical Bayes berbasis model Beta-Binomial. hal ini dilakukan karena informasi yang dikeluarkan oleh Badan pusat Statistik (BPS) tahun 2016 hanya angka pengangguran tingkat provinsi dan tidak tersedia data untuk tingkat kabupaten/kota. Penelitian ini menggunakan data BPS, yaitu jumlah pengangguran dan jumlah angkatan kerja di kabupaten/kota di Sumatera Barat. Penelitian ini menghasilkan nilai dugaan angka pengangguran kabupaten/kota di Sumatera Barat dengan metode langsung dan metode Empirical Bayes. Dengan demikian dapat disimpulkan bahwa penduga menggunakan metode Empirical Bayes lebih baik dari metode langsung.Diterima: Direvisi: Dipublikasikan :Kata Kunci: Small Area Estimation, Empirical Bayes, Angka Pengangguran.

scholarly journals Simulation Study of Robust Geographically Weighted Empirical Best Linear Unbiased Predictor on Small Area Estimation

2021 ◽  
Vol 5 (1) ◽  
pp. 50-60
Author(s):  
Naima Rakhsyanda ◽  
Kusman Sadik ◽  
Indahwati Indahwati
Keyword(s):  
Small Area ◽  
Spatial Information ◽  
Mean Squared Error ◽  
Small Area Estimation ◽  
Small Sample ◽  
Population Parameter ◽  
Spatial Effects ◽  
Best Linear Unbiased Predictor ◽  
Area Estimation ◽  
Best Linear Unbiased

Small area estimation can be used to predict the population parameter with small sample sizes. For some cases, the population units that are close spatially may be more related than units that are further apart. The use of spatial information like geographic coordinates are studied in this research. Outlier contaminations can affect small area estimations. This study was conducted using simulation methods on generated data with six scenarios. The scenarios are the combination of spatial effects (spatial stationary and spatial non-stationary) with outlier contamination (no outlier, symmetric outliers, and non-symmetric outliers). The purpose of this study was to compare the geographically weighted empirical best linear unbiased predictor (GWEBLUP) and robust GWEBLUP (RGWEBLUP) with direct estimator, EBLUP, and REBLUP using simulation data. The performance of the predictors is evaluated using relative root mean squared error (RRMSE). The simulation results showed that geographically weighted predictors have the smallest RRMSE values for scenarios with spatial non-stationary, therefore offer a better prediction. For scenarios with outliers, robust predictors with smaller RRMSE values offer more efficiency than non-robust predictors.

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