scholarly journals Development and Evaluation of an Optimal Composite Estimator in Spatial Microsimulation Small Area Estimation

2019 ◽  
Vol 52 (3) ◽  
pp. 351-370
Author(s):  
Angelo Moretti ◽  
Adam Whitworth
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Spatial Microsimulation ◽  
Composite Estimator

Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation

2009 ◽  
Vol 3 (2) ◽  
pp. 3-22 ◽  
Author(s):  
Azizur Rahman ◽  
Ann Harding ◽  
Robert Tanton ◽  
Shuangzhe Liu
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Methodological Issues ◽  
Area Estimation ◽  
Spatial Microsimulation

The Challenges of Combining Two Databases in Small-Area Estimation: An Example Using Spatial Microsimulation of Child Poverty

10.1068/a4511 ◽  
2013 ◽  
Vol 45 (2) ◽  
pp. 344-361 ◽  
Author(s):  
Yogi Vidyattama ◽  
Riyana Miranti ◽  
Justine McNamara ◽  
Robert Tanton ◽  
Ann Harding
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Child Poverty ◽  
Area Estimation ◽  
Spatial Microsimulation

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

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 [Small Area Estimation: An Appraisal]: Rejoinder

1994 ◽  
Vol 9 (1) ◽  
pp. 90-93 ◽  
Author(s):  
M. Ghosh ◽  
J. N. K. Rao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation

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

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.

Sign in / Sign up

Export Citation Format

Share Document