scholarly journals Incorporating Design Weights And Historical Data Into Model-Based Small-Area Estimation

2021 ◽  
pp. 115-131
Author(s):  
Hui Xie ◽  
Lawrence E. Barker ◽  
Deborah B. Rolka
Keyword(s):  
Small Area ◽  
Historical Data ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Model Based ◽  
Design Weights

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 Model-Based Estimators Using Big Data Sources

2015 ◽  
Vol 31 (2) ◽  
pp. 263-281 ◽  
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.

scholarly journals Small-Area Estimation for the USDA Forest Service, National Woodland Owner Survey: Creating a Fine-Scale Land Cover and Ownership Layer to Support County-Level Population Estimates

2021 ◽  
Vol 4 ◽  
Author(s):  
Vance Harris ◽  
Jesse Caputo ◽  
Andrew Finley ◽  
Brett J. Butler ◽  
Forrest Bowlick ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Small Area ◽  
Small Area Estimation ◽  
Fine Scale ◽  
Ancillary Data ◽  
United States Census Bureau ◽  
Area Estimation ◽  
Model Based ◽  
National Woodland Owner Survey

Small area estimation is a powerful modeling technique in which ancillary data can be utilized to “borrow” additional information, effectively increasing sample sizes in small spatial, temporal, or categorical domains. Though more commonly applied to biophysical variables within the study of forest inventory analyses, small area estimation can also be implemented in the context of understanding social values, behaviors, and trends among types of forest landowners within small domains. Here, we demonstrate a method for deriving a continuous fine-scale land cover and ownership layer for the state of Delaware, United States, and an application of that ancillary layer to facilitate small-area estimation of several variables from the USDA Forest Service’s National Woodland Owner Survey. Utilizing a proprietary parcel layer alongside the National Land Cover Database, we constructed a continuous layer with 10-meter resolution depicting land cover and land ownership classes. We found that the National Woodland Owner Survey state-level estimations of total acreage and total ownerships by ownership class were generally within one standard error of the population values calculated from the raster layer, which supported the direct calculation of several population-level summary variables at the county levels. Subsequently, we compare design-based and model-based methods of predicting commercial harvesting by family forest ownerships in Delaware in which forest ownership acreage, taken from the parcel map, was utilized to inform the model-based approach. Results show general agreement between the two modes, indicating that a small area estimation approach can be utilized successfully in this context and shows promise for other variables, especially if additional variables, e.g., United States Census Bureau data, are also incorporated.

scholarly journals Non-parametric bootstrap and small area estimation to mitigate bias in crowdsourced data. Simulation study and application to perceived safety

2019 ◽  
Author(s):  
David Buil-Gil ◽  
Reka Solymosi ◽  
Angelo Moretti
Keyword(s):  
Simulation Study ◽  
Small Area ◽  
Small Area Estimation ◽  
Parametric Bootstrap ◽  
Area Estimation ◽  
Model Based ◽  
Safety Perceptions ◽  
Crowdsourced Data ◽  
Level Model ◽  
Non Parametric

Open and crowdsourced data are becoming prominent in social sciences research. Crowdsourcing projects harness information from large crowds of citizens who voluntarily participate into one collaborative project, and allow new insights into people’s attitudes and perceptions. However, these are usually affected by a series of biases that limit their representativeness (i.e. self-selection bias, unequal participation, underrepresentation of certain areas and times). In this chapter we present a two-step method aimed to produce reliable small area estimates from crowdsourced data when no auxiliary information is available at the individual level. A non-parametric bootstrap, aimed to compute pseudosampling weights and bootstrap weighted estimates, is followed by an area-level model based small area estimation approach, which borrows strength from related areas based on a set of covariates, to improve the small area estimates. In order to assess the method, a simulation study and an application to safety perceptions in Greater London are conducted. The simulation study shows that the area-level model-based small area estimator under the non-parametric bootstrap improves (in terms of bias and variability) the small area estimates in the majority of areas. The application produces estimates of safety perceptions at a small geographical level in Greater London from Place Pulse 2.0 data. In the application, estimates are validated externally by comparing these to reliable survey estimates. Further simulation experiments and applications are needed to examine whether this method also improves the small area estimates when the sample biases are larger, smaller or show different distributions. A measure of reliability also needs to be developed to estimate the error of the small area estimates under the non-parametric bootstrap.

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

2018 ◽  
Vol 28 (7) ◽  
pp. 481-488.e4 ◽  
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
Sign in / Sign up

Export Citation Format

Share Document