scholarly journals A Study of Small Area Estimation for Italian Structural Business Statistics

2018 ◽  
Vol 34 (2) ◽  
pp. 543-555
Author(s):  
Orietta Luzi ◽  
Fabrizio Solari ◽  
Fabiana Rocci
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Optimal Estimation ◽  
Estimation Strategy ◽  
Area Estimation ◽  
Statistical Institute ◽  
Business Statistics ◽  
Different Types ◽  
Quality And Reliability

Abstract The Frame SBS is a statistical register which has been developed at the Italian National Statistical Institute to support the annual estimation of structural business statistics (SBS). Actually, a number of core SBS are estimated by combining microdata directly supplied by different administrative sources. In this context, more accurate estimates for those SBS that are not covered by administrative sources can be obtained through small area estimation (SAE). In this article, we illustrate an application of SAE methods in the framework of the Frame SBS register in order to assess the potential advantages that can be achieved in terms of increased quality and reliability of the target variables. Different types of auxiliary information and approaches are compared in order to identify the optimal estimation strategy in terms of precision of the estimates.

scholarly journals Analysis of area level and unit level models for small area estimation in forest inventories assisted with LiDAR auxiliary information

PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189401 ◽  
Author(s):  
Francisco Mauro ◽  
Vicente J. Monleon ◽  
Hailemariam Temesgen ◽  
Kevin R. Ford
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Area Estimation ◽  
Unit Level ◽  
Forest Inventories

scholarly journals Small area estimation strategy for the 2011 Census in England and Wales

2018 ◽  
Vol 34 (3) ◽  
pp. 395-407 ◽  
Author(s):  
Bernard Baffour ◽  
Denise Silva ◽  
Alinne Veiga ◽  
Christine Sexton ◽  
James J. Brown
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
England And Wales ◽  
Estimation Strategy ◽  
Area Estimation

Applications of Small Area Estimation to Generalization With Subclassification by Propensity Scores

2017 ◽  
Vol 43 (2) ◽  
pp. 182-224
Author(s):  
Wendy Chan
Keyword(s):  
Propensity Score ◽  
Small Area ◽  
Propensity Scores ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Population Data ◽  
Small Sample ◽  
Estimation Methods ◽  
Sample Sizes ◽  
Area Estimation

Policymakers have grown increasingly interested in how experimental results may generalize to a larger population. However, recently developed propensity score–based methods are limited by small sample sizes, where the experimental study is generalized to a population that is at least 20 times larger. This is particularly problematic for methods such as subclassification by propensity score, where limited sample sizes lead to sparse strata. This article explores the potential of small area estimation methods to improve the precision of estimators in sparse strata using population data as a source of auxiliary information to borrow strength. Results from simulation studies identify the conditions under which small area estimators outperform conventional estimators and the limitations of this application to causal generalization studies.

scholarly journals Auxiliary information resolution effects on small area estimation in plantation forest inventory

2020 ◽  
Vol 93 (5) ◽  
pp. 685-693
Author(s):  
P Corey Green ◽  
Harold E Burkhart ◽  
John W Coulston ◽  
Philip J Radtke ◽  
Valerie A Thomas
Keyword(s):  
Loblolly Pine ◽  
Forest Inventory ◽  
Small Area ◽  
Point Cloud ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Plantation Forest ◽  
Area Estimation ◽  
Improve Estimate ◽  
Elevation Model

Abstract In forest inventory, traditional ground-based resource assessments are often expensive and time-consuming forcing managers to reduce sample sizes to meet budgetary and logistical constraints. Small area estimation (SAE) is a class of statistical estimators that uses a combination of traditional survey data and linearly related auxiliary information to improve estimate precision. These techniques have been shown to improve the precision of stand-level inventory estimates in loblolly pine plantations using lidar height percentiles and thinning status as covariates. In this study, the effects of reduced lidar point-cloud densities and lower digital elevation model (DEM) spatial resolutions were investigated for total planted volume estimates using area-level SAE models. In the managed Piedmont pine plantation conditions evaluated, lower lidar point-cloud densities and DEM spatial resolutions were found to have minimal effects on estimates and precision. The results of this study are promising to those interested in incorporating SAE methods into forest inventory programs.

A comparison of small-area estimation techniques to estimate selected stand attributes using LiDAR-derived auxiliary variables

2011 ◽  
Vol 41 (6) ◽  
pp. 1189-1201 ◽  
Author(s):  
Michael E. Goerndt ◽  
Vicente J. Monleon ◽  
Hailemariam Temesgen
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Estimation Methods ◽  
Stem Volume ◽  
Small Areas ◽  
Forest Density ◽  
Area Estimation ◽  
Forest Attributes ◽  
Areas Of Interest

One of the challenges often faced in forestry is the estimation of forest attributes for smaller areas of interest within a larger population. Small-area estimation (SAE) is a set of techniques well suited to estimation of forest attributes for small areas in which the existing sample size is small and auxiliary information is available. Selected SAE methods were compared for estimating a variety of forest attributes for small areas using ground data and light detection and ranging (LiDAR) derived auxiliary information. The small areas of interest consisted of delineated stands within a larger forested population. Four different estimation methods were compared for predicting forest density (number of trees/ha), quadratic mean diameter (cm), basal area (m2/ha), top height (m), and cubic stem volume (m3/ha). The precision and bias of the estimation methods (synthetic prediction (SP), multiple linear regression based composite prediction (CP), empirical best linear unbiased prediction (EBLUP) via Fay–Herriot models, and most similar neighbor (MSN) imputation) are documented. For the indirect estimators, MSN was superior to SP in terms of both precision and bias for all attributes. For the composite estimators, EBLUP was generally superior to direct estimation (DE) and CP, with the exception of forest density.

scholarly journals A novel application of small area estimation in loblolly pine forest inventory

2019 ◽  
Vol 93 (3) ◽  
pp. 444-457
Author(s):  
P Corey Green ◽  
Harold E Burkhart ◽  
John W Coulston ◽  
Philip J Radtke
Keyword(s):  
Loblolly Pine ◽  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Parameter Estimates ◽  
Area Estimation ◽  
Unit Level ◽  
Stand Thinning ◽  
Pinus Taeda L ◽  
Volume Estimates

Abstract Loblolly pine (Pinus taeda L.) is one of the most widely planted tree species globally. As the reliability of estimating forest characteristics such as volume, biomass and carbon becomes more important, the necessary resources available for assessment are often insufficient to meet desired confidence levels. Small area estimation (SAE) methods were investigated for their potential to improve the precision of volume estimates in loblolly pine plantations aged 9–43. Area-level SAE models that included lidar height percentiles and stand thinning status as auxiliary information were developed to test whether precision gains could be achieved. Models that utilized both forms of auxiliary data provided larger gains in precision compared to using lidar alone. Unit-level SAE models were found to offer additional gains compared with area-level models in some cases; however, area-level models that incorporated both lidar and thinning status performed nearly as well or better. Despite their potential gains in precision, unit-level models are more difficult to apply in practice due to the need for highly accurate, spatially defined sample units and the inability to incorporate certain area-level covariates. The results of this study are of interest to those looking to reduce the uncertainty of stand parameter estimates. With improved estimate precision, managers, stakeholders and policy makers can have more confidence in resource assessments for informed decisions.

Small-area estimates using model-based methods

1996 ◽  
Vol 26 (5) ◽  
pp. 758-766 ◽  
Author(s):  
Annika Kangas
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Parametric Models ◽  
Estimation Methods ◽  
Small Areas ◽  
Area Estimation ◽  
Numerical Example ◽  
Model Based

In small areas, the number of sample plots is usually small, and the classical estimators have a large variance. Information from nearby areas can be utilized to improve the subarea estimates using either nonparametric or parametric models. In this study, a number of model-based estimators for small-area estimation are presented. To illustrate the presented methods a numerical example in a real inventory situation is given. The auxiliary information used in this study is pure coordinate information, but the methods are applicable also for other kinds of auxiliary information. The object of this study is to compare the features of the presented small-area estimation methods and to discuss the applicability of these methods in different situations.

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