An Alternative Procedure to Produce a P-Spline Small Area Estimation Model Based on Partial Residual Plot and Significance Test of Spline Term

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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Estimation of unemployment rates using small area estimation model by combining time series and cross-sectional data

10.1063/1.4940870 ◽

2016 ◽

Cited By ~ 1

Author(s):

Siti Muchlisoh ◽

Anang Kurnia ◽

Khairil Anwar Notodiputro ◽

I. Wayan Mangku

Keyword(s):

Time Series ◽

Small Area ◽

Small Area Estimation ◽

Estimation Model ◽

Cross Sectional ◽

Unemployment Rates ◽

Area Estimation

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Expenditure Per Capita Model with Spatial Small Area Estimation

Parameter: Journal of Statistics ◽

10.22487/27765660.2021.v1.i2.15502 ◽

2021 ◽

Vol 1 (2) ◽

pp. 38-47

Author(s):

Ahmad Risal

Keyword(s):

Small Area ◽

Spatial Information ◽

Small Area Estimation ◽

Random Effect ◽

Development Program ◽

Data Availability ◽

Estimation Model ◽

Policy Makers ◽

Area Estimation ◽

Direct Estimation

Indonesia is one of many countries around the world that attempt to suffer from high poverty rates. Since, poverty information in a certain area is a point of interest to researchers and policy makers. One problem faced is for the development program to be carried out more effectively and efficiently, it is necessary to have data availability up to the micro-scale. The technique used to reach the goal is Small Area Estimation (SAE). Fay-herriot (FH) model is one method on Small Area Estimation. Since, the SAE techniques require “borrow strength” across neighbor areas so thus Fay-Herriot model approach was developed by integrating spatial information into the model. This method known as Spatial Fay-Herriot Model (SFH) or Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). This study aims to compare MSE of direct estimation, FH, and SFH Model to see which method gives the best result in estimating expenditure. The MSE value of the estimated SFH is smaller than direct estimation and FH, but it does not significant. It means adding spatial information in the small area estimation model does not give a better prediction than the simple small area estimation which is takes account the area as a specific random effect.

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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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