Comparison of the k-nearest neighbor technique with geographical calibration for estimating forest growing stock volumeThis article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time.

2011 ◽  
Vol 41 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Jong Su Yim ◽  
Young Hwan Kim ◽  
Sung Ho Kim ◽  
Jin Hyun Jeong ◽  
Man Yong Shin
Keyword(s):  
Sample Size ◽  
Small Area ◽  
Field Data ◽  
Nearest Neighbor ◽  
Small Area Estimation ◽  
K Nearest Neighbor ◽  
Area Estimation ◽  
Growing Stock ◽  
Significant Difference ◽  
Stock Volume

National Forest Inventories (NFIs) have been used in many countries to assess forest resources at the national level. To facilitate the estimation of forest growing stock volume at more regional scales, the k-nearest neighbor (k-NN) technique was applied in this research to obtain estimates for unmeasured areas by using NFI field data and optical satellite data. The NFI field data were assigned to data sets of three different sample sizes to evaluate the effect of sample size on the accuracy of k-NN estimates. In small-area estimation, calibration techniques, in which samples surveyed outside a county of interest are employed to produce estimates for the county, are often adopted due to the lack of sample observations for the county of interest. Thus, the k-NN estimates, forest growing stock volume and areal proportions by forest types, were compared with estimates obtained from field data with and without calibration. The results indicated that the accuracy of k-NN estimates could be improved as sample size increased. Also, the k-NN technique provided acceptable estimates for small-area estimation. Although there was no significant difference with the calibration approach (p > 0.18), k-NN has potential for small-area estimation and is useful to generate thematic maps of forest attributes.

scholarly journals Binomial Regression in Small Area Estimation Method for Estimate Proportion of Cultural Indicator

2018 ◽  
Author(s):  
Yudistira Yudistira ◽  
Anang Kurnia ◽  
Agus Mohamad Soleh
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Small Area ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Alternative Methods ◽  
Area Estimation ◽  
Binomial Regression ◽  
Cultural Indicator ◽  
Sampling Survey

In sampling survey, it was necessary to have sufficient sample size in order to get accurate direct estimator about parameter, but there are many difficulties to fulfill them in practice. Small Area Estimation (SAE) is one of alternative methods to estimate parameter when sample size is not adequate. This method has been widely applied in such variation of model and many fields of research. Our research mainly focused on study how SAE method with binomial regression model is applied to obtained estimate proportion of cultural indicator, especially to estimate proportion of people who appreciate heritages and museums in each regency/city level in West Java Province. Data analysis approach used in our research with resurrected data and variables in order to be compared with previous research. The result later showed that binomial regression model could be used to estimate proportion of cultural indicator in Regency/City in Indonesia with better result than direct estimation method.

scholarly journals BINOMIAL REGRESSION IN SMALL AREA ESTIMATION METHOD FOR ESTIMATE PROPORTION OF CULTURAL INDICATOR

2018 ◽  
Vol 2 (2) ◽  
pp. 56-62
Author(s):  
Yudistira Yudistira ◽  
Anang Kurnia ◽  
Agus Mohamad Soleh
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Small Area ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Alternative Methods ◽  
Area Estimation ◽  
Binomial Regression ◽  
Cultural Indicator ◽  
Sampling Survey

In sampling survey, it was necessary to have sufficient sample size in order to get accurate direct estimator about parameter, but there are many difficulties to fulfill them in practice. Small Area Estimation (SAE) is one of alternative methods to estimate parameter when sample size is not adequate. This method has been widely applied in such variation of model and many fields of research. Our research mainly focused on study how SAE method with binomial regression model is applied to obtained estimate proportion of cultural indicator, especially to estimate proportion of people who appreciate heritages and museums in each regency/city level in West Java Province. Data analysis approach used in our research with resurrected data and variables in order to be compared with previous research. The result later showed that binomial regression model could be used to estimate proportion of cultural indicator in Regency/City in Indonesia with better result than direct estimation method.

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.

Multinomial-based small area estimation of labour force indicators

2013 ◽  
Vol 13 (2) ◽  
pp. 153-178 ◽  
Author(s):  
Esther López-Vizcaíno ◽  
María José Lombardía ◽  
Domingo Morales
Keyword(s):  
Small Area ◽  
Labour Force ◽  
Small Area Estimation ◽  
Area Estimation
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