Robust Bayesian small area estimation based on quantile regression

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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[Small Area Estimation: An Appraisal]: Rejoinder

Statistical Science ◽

10.1214/ss/1177010654 ◽

1994 ◽

Vol 9 (1) ◽

pp. 90-93 ◽

Cited By ~ 12

Author(s):

M. Ghosh ◽

J. N. K. Rao

Keyword(s):

Small Area ◽

Small Area Estimation ◽

Area Estimation

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Small area estimation using survey weights under a nested error linear regression model with structural measurement error

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2012.02.015 ◽

2012 ◽

Vol 109 ◽

pp. 52-60 ◽

Cited By ~ 7

Author(s):

Mahmoud Torabi

Keyword(s):

Measurement Error ◽

Linear Regression ◽

Regression Model ◽

Linear Regression Model ◽

Small Area ◽

Small Area Estimation ◽

Area Estimation

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Enhancing the precision of broad-scale forestland removals estimates with small area estimation techniques

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpaa045 ◽

2021 ◽

Author(s):

John W Coulston ◽

P Corey Green ◽

Philip J Radtke ◽

Stephen P Prisley ◽

Evan B Brooks ◽

...

Keyword(s):

Small Area ◽

Small Area Estimation ◽

Species Group ◽

Tree Cover ◽

Estimation Techniques ◽

Spatial And Temporal Scales ◽

Area Estimation ◽

Forest Inventories ◽

Southeastern Us ◽

Increasing Demand

Abstract National Forest Inventories (NFI) are designed to produce unbiased estimates of forest parameters at a variety of scales. These parameters include means and totals of current forest area and volume, as well as components of change such as means and totals of growth and harvest removals. Over the last several decades, there has been a steadily increasing demand for estimates for smaller geographic areas and/or for finer temporal resolutions. However, the current sampling intensities of many NFI and the reliance on design-based estimators often leads to inadequate precision of estimates at these scales. This research focuses on improving the precision of forest removal estimates both in terms of spatial and temporal resolution through the use of small area estimation techniques (SAE). In this application, a Landsat-derived tree cover loss product and the information from mill surveys were used as auxiliary data for area-level SAE. Results from the southeastern US suggest improvements in precision can be realized when using NFI data to make estimates at relatively fine spatial and temporal scales. Specifically, the estimated precision of removal volume estimates by species group and size class was improved when SAE methods were employed over post-stratified, design-based estimates alone. The findings of this research have broad implications for NFI analysts or users interested in providing estimates with increased precision at finer scales than those generally supported by post-stratified estimators.

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