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

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.

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Simple House Needs in Jember with Robust Small Area Estimation

Jurnal ILMU DASAR ◽

10.19184/jid.v18i1.3159 ◽

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.

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Robust Small Area Estimation: An Overview

Annual Review of Statistics and Its Application ◽

10.1146/annurev-statistics-031219-041212 ◽

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.

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Estimating county-level tobacco use and exposure in South Carolina: a spatial model-based small area estimation approach

Annals of Epidemiology ◽

10.1016/j.annepidem.2018.03.015 ◽

2018 ◽

Vol 28 (7) ◽

pp. 481-488.e4 ◽

Cited By ~ 2

Author(s):

Jan M. Eberth ◽

Alexander C. McLain ◽

Yuan Hong ◽

Erica Sercy ◽

Abdoulaye Diedhiou ◽

...

Keyword(s):

South Carolina ◽

Tobacco Use ◽

Spatial Model ◽

Small Area ◽

Small Area Estimation ◽

County Level ◽

Area Estimation ◽

Model Based

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

Indonesian Journal of Statistics and Its Applications ◽

10.29244/ijsa.v5i1p50-60 ◽

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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Hierarchical Bayesian models for small area estimation of county-level private forest landowner population

Canadian Journal of Forest Research ◽

10.1139/cjfr-2017-0154 ◽

2017 ◽

Vol 47 (12) ◽

pp. 1577-1589 ◽

Cited By ~ 1

Author(s):

Neil R. Ver Planck ◽

Andrew O. Finley ◽

Emily S. Huff

Keyword(s):

Population Size ◽

Small Area ◽

Mean Squared Error ◽

Small Area Estimation ◽

Model Performance ◽

Remote Sensing Data ◽

Level Population ◽

County Level ◽

Private Forest ◽

Area Estimation

The National Woodland Owner Survey (NWOS), administered by the USDA Forest Service, provides estimates of private forest ownership characteristics and owners’ attitudes and behaviors at a national, regional, and state levels. Due to sample sizes prescribed for inference at the state level, there are insufficient data to support county-level estimates. However, county-level estimates of NWOS variables are desired because ownership programs and education initiatives often occur at the county level and such information could help tailor these efforts to better match county-specific needs and demographics. Here, we present and assess methods to estimate the number of private forest ownerships at the county level for two states, Montana and New Jersey. To assess model performance, true population parameters were derived from cadastral and remote sensing data. Two small area estimation (SAE) models, the Fay-Herriot (FH) and the FH with conditional autoregressive random effects (FHCAR), improved estimated county-level population mean squared error (MSE) over that achieved by direct estimates. The proposed SAE models use covariates to improve accuracy and precision of county-level estimates. Results show total forest area, and 2010 decennial census population density covariates explained a significant portion of variability in county-level population size. These and other results suggest that the proposed SAE methods yield a statistically robust approach to deliver reliable estimates of private ownership population size and could be extended to additional important NWOS variables at the county level.

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Small Area Model-Based Estimators Using Big Data Sources

Journal of Official Statistics ◽

10.1515/jos-2015-0017 ◽

2015 ◽

Vol 31 (2) ◽

pp. 263-281 ◽

Cited By ~ 31

Author(s):

Stefano Marchetti ◽

Caterina Giusti ◽

Monica Pratesi ◽

Nicola Salvati ◽

Fosca Giannotti ◽

...

Keyword(s):

Data Mining ◽

Big Data ◽

Small Area ◽

Small Area Estimation ◽

The Body ◽

Statistical Research ◽

Social Data ◽

Area Estimation ◽

Model Based ◽

Social Data Mining

Abstract The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.

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