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

Multivariate Fay-Herriot models for small area estimation with application to household consumption per capita expenditure in Indonesia

2019 ◽  
Vol 46 (15) ◽  
pp. 2845-2861 ◽  
Author(s):  
Azka Ubaidillah ◽  
Khairil Anwar Notodiputro ◽  
Anang Kurnia ◽  
I. Wayan Mangku
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Household Consumption ◽  
Area Estimation ◽  
Capita Expenditure ◽  
Per Capita

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.

scholarly journals The Poverty Modeling Using Small Area Estimation with Semiparametric P-Spline (A case study: Poverty in Bengkulu Province)

2018 ◽  
Vol 6 (4) ◽  
Author(s):  
Idhia Sriliana ◽  
Etis Sunandi ◽  
Ulfasari Rafflesia
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Accurate Information ◽  
Model Parameters ◽  
Mixed Effect ◽  
Area Estimation ◽  
Capita Expenditure ◽  
Nonparametric Approach ◽  
Penalized Spline ◽  
Semiparametric Approach

The main objective of this research is to model poverty in Bengkulu Province using small area estimation (SAE) with semiparametric penalized spline (P-Spline). Small area estimation is a statistical method that is often used to obtain an accurate information about poverty. When the linearity assumption on the basic SAE model is violated, a nonparametric approach is used as an alternative. One is the semiparametric  penalized spline. The small area  method with semiparametric approach has a more flexible model because it accommodates the relationship between response with linear and nonlinear predictors. In this study, poverty modeling in Bengkulu Province was based on average per capita expenditure through the estimation of SAE model parameters using semiparametric P-Spline to obtain a mixed-effect model regression equation as a poverty model. Based on the analysis result, the poverty model in Bengkulu Province is P-Spline linear model with one knot. This model has a GCV value of 148928361265.95. Poverty mapping in Bengkulu Province based on sample villages indicates the estimation of poverty using SAE model with P-Spline having the same trend with the direct estimator.

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