Small area estimation of labour force indicators under a multinomial model with correlated time and area effects

2014 ◽  
Vol 178 (3) ◽  
pp. 535-565 ◽  
Author(s):  
Esther López-Vizcaíno ◽  
María José Lombardía ◽  
Domingo Morales
Keyword(s):  
Small Area ◽  
Labour Force ◽  
Small Area Estimation ◽  
Multinomial Model ◽  
Area Estimation ◽  
Area Effects

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

National forest inventories in the service of small area estimation of stem volume

2014 ◽  
Vol 44 (9) ◽  
pp. 1079-1090 ◽  
Author(s):  
Steen Magnussen ◽  
Daniel Mandallaz ◽  
Johannes Breidenbach ◽  
Adrian Lanz ◽  
Christian Ginzler
Keyword(s):  
Model Selection ◽  
Small Area ◽  
Small Area Estimation ◽  
Model Averaging ◽  
Stem Volume ◽  
National Forest ◽  
Area Estimation ◽  
Forest Inventories ◽  
Area Effects ◽  
National Forest Inventories

This study introduces five facets that can improve inference in small area estimation (SAE) problems: (1) model groups, (2) test of area effects, (3) conditional EBLUPs, (4) model selection, and (5) model averaging. Two contrasting case studies with data from the Swiss and Norwegian national forest inventories demonstrate the five facets. The target variable of interest was mean stem volume per hectare on forested land in 108 Swiss forest districts (FD) and in 14 Norwegian municipalities (KOM) in the County of Vestfold. Auxiliary variables from airborne laser scanning (Switzerland) and photogrammetric point clouds (Vestfold) with full coverage and a resolution of 25 m × 25 m (Switzerland) and 16 m × 16 m (Vestfold) were available. Only the data metric mean canopy height was statistically significant. Ten linear fixed-effects models and three mixed linear models were assessed. Area effects were statistically significant in the Swiss case but not in Vestfold case. A model selection based on AIC favored separate linear regression models for each FD and a single common regression model in Vestfold. Model averaging increased, on average, an estimated variance by 15%. Reported estimates of uncertainty were consistently larger than corresponding unconditional EBLUPs.

Small area estimation at provincial level in the Italian labour force survey

1998 ◽  
Vol 7 (1) ◽  
pp. 93-109 ◽  
Author(s):  
Piero Demetrio Falorsi ◽  
Stefano Falorsi ◽  
Aldo Russo
Keyword(s):  
Small Area ◽  
Labour Force ◽  
Small Area Estimation ◽  
Labour Force Survey ◽  
Area Estimation ◽  
Provincial Level

scholarly journals Small Area Estimation of Sub-District’s Per Capita Expenditure through Area Effects Selection using LASSO Method

2021 ◽  
Vol 179 ◽  
pp. 754-761
Author(s):  
Novi Hidayat Pusponegoro ◽  
Anang Kurnia ◽  
Khairil Anwar Notodiputro ◽  
Agus Mohamad Soleh ◽  
Erni Tri Astuti
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Capita Expenditure ◽  
Area Effects ◽  
Per Capita ◽  
Lasso 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
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