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 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 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.

scholarly journals Penerapan Empirical Best Linear Unbiased Prediction (EBLUP) pada Pendugaan Tingkat Pengangguran Terbuka Level Kecamatan di Provinsi Banten

2021 ◽  
Vol 2021 (1) ◽  
pp. 36-44
Author(s):  
Apriliansyah - ◽  
Ika Yuni Wulansari
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Pengangguran merupakan masalah yang kompleks baik disebabkan maupun memberikan dampak terhadap banyak faktor. Di Indonesia, indikator pengangguran diukur melalui tingkat pengangguran terbuka (TPT). Indikator ini dikumpulkan melalui Survei Angkatan Kerja Nasional (Sakernas). Banten merupakan provinsi dengan TPT tertinggi serta selalu masuk dalam lima besar TPT tertinggi di Indonesia sejak tahun 2016 hingga 2018. Sebagai upaya percepatan penurunan angka pengangguran, perlu adanya informasi sampai ke level terkecil. Namun, sampel yang digunakan oleh Sakernas tidak mencukupi untuk pendugaan TPT secara langsung. Tujuan dari penelitian ini adalah menghasilkan penduga tingkat pengangguran terbuka (TPT) level kecamatan dengan presisi yang lebih baik melalui Small Area Estimation (SAE). Metode SAE yang digunakan adalah Empirical Best Linear Unbiased Prediction (EBLUP). Data yang digunakan adalah indikator penyusun TPT dari Sakernas 2018 dan variabel penyerta yang berasal dari Podes 2018 Provinsi Banten. Hasil dari penelitian ini menunjukkan bahwa terdapat sepuluh variabel penyerta yang berkorelasi dengan TPT. Kemudian, nilai RRMSE menunjukkan bahwa EBLUP meningkatkan presisi pendugaan jika dibandingkan dengan penduga langsung. Terdapat dua kecamatan yang memiliki hasil TPT sangat tinggi yaitu kecamatan Curugbitung dan Koroncong. Hasil estimasi EBLUP pada TPT level kecamatan ini dapat digunakan oleh pemerintah daerah untuk menurunkan TPT secara lebih terfokus dan lebih tepat sasaran, utamanya pada kecamatan-kecamatan dengan TPT yang tinggi.

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.

Robust Small Area Estimation: An Overview

2020 ◽  
Vol 7 (1) ◽  
pp. 337-360
Author(s):  
Jiming Jiang ◽  
J. Sunil Rao
Keyword(s):  
Small Area ◽  
Small Sample Size ◽  
Small Area Estimation ◽  
Small Sample ◽  
Direct Estimate ◽  
Bayesian Approaches ◽  
Area Estimation ◽  
Domain Specific ◽  
Software Packages ◽  
Model Failure

A small area typically refers to a subpopulation or domain of interest for which a reliable direct estimate, based only on the domain-specific sample, cannot be produced due to small sample size in the domain. While traditional small area methods and models are widely used nowadays, there have also been much work and interest in robust statistical inference for small area estimation (SAE). We survey this work and provide a comprehensive review here. We begin with a brief review of the traditional SAE methods. We then discuss SAE methods that are developed under weaker assumptions and SAE methods that are robust in certain ways, such as in terms of outliers or model failure. Our discussion also includes topics such as nonparametric SAE methods, Bayesian approaches, model selection and diagnostics, and missing data. A brief review of software packages available for implementing robust SAE methods is also given.

scholarly journals Small Area Estimation on Zero-Inflated Data Using Frequentist and Bayesian Approach

2020 ◽  
Vol 18 (1) ◽  
pp. 2-22
Author(s):  
Kusman Sadik ◽  
Rahma Anisa ◽  
Euis Aqmaliyah
Keyword(s):  
Bayesian Approach ◽  
Small Area ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Small Area Estimation ◽  
Prediction Method ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Best Linear Unbiased ◽  
Sr Method

The most commonly used method of small area estimation (SAE) is the empirical best linear unbiased prediction method based on a linear mixed model. However, it is not appropriate in the case of the zero-inflated target variable with a mixture of zeros and continuously distributed positive values. Therefore, various model-based SAE methods for zero-inflated data are developed, such as the Frequentist approach and the Bayesian approach. Both approaches are compared with the survey regression (SR) method which ignores the presence of zero-inflation in the data. The results show that the two SAE approaches for zero-inflated data are capable to yield more accurate area mean estimates than the SR method.

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 untuk Estimasi Angka Kematian Bayi di Indonesia menggunakan Metode Empirical Best Linear Unbiased Prediction

2021 ◽  
Vol 10 (2) ◽  
pp. 171
Author(s):  
Nadra Yudelsa Ratu ◽  
Easbi Ikhsan
Keyword(s):  
Sustainable Development ◽  
Small Area ◽  
Sustainable Development Goals ◽  
Small Area Estimation ◽  
Best Linear Unbiased Prediction ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Development Goals ◽  
Best Linear Unbiased ◽  
Unbiased Prediction

Angka Kematian Bayi (AKB) adalah jumlah kematian bayi usia di bawah satu tahun untuk setiap 1000 kelahiran bayi lahir hidup dalam kurun waktu satu tahun. IMR merupakan indikator penting dari status kesehatan dari masyarakat dalam suatu daerah. Hal ini sejalan dengan Sustainable Development Goals (SDG’s) yang ke tiga yaitu memastikan kehidupan yang sehat dan mendukung kesejahteraan bagi semua untuk semua usia. AKB dihasilkan melalui estimasi langsung dari Survei Demografi dan Kesehatan Indonesia (SDKI). Akan tetapi, dalam SDKI 2017, AKB hanya bisa menghasilkan indikator pada level nasional. Hal ini disebabkan estimasi langsung dari AKB di beberapa provinsi memiliki nilai Relatif Standard Error (RSE) yang besar dan ukuran sampel yang tidak mencukupi. Dalam jurnal ini, kami mempelajari Small Area Estimation (SAE) menggunakan metode Empirical Best Linear Unbiased Prediction (EBLUP) level area untuk mengatasi keterbatasan estimasi AKB di level provinsi. SAE dilakukan dengan meminjam kekuatan beberapa variabel dari data Potensi Desa (PODES) yang berkorelasi kuat dengan AKB tingkat provinsi di Indonesia. Hasil penelitian menunjukkan bahwa SAE menggunakan metode EBLUP memiliki nilai RSE yang lebih kecil dibandingkan estimasi langsung dari SDKI. Sehingga, dapat dikatakan bahwa SAE menggunakan metode EBLUP baik untuk memperkirakan AKB level provinsi di Indonesia pada tahun 2017.

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