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

Sriliana, Idhia; Sunandi, Etis; Rafflesia, Ulfasari

doi:10.24203/ajas.v6i4.5355

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Non-Stationarity: The concept of nonstationarity refers to changes in the relationships between variables spatially or temporally. MGWR can capture this non-stationarity better than regular GWR, because it can model spatially and temporally varying effects Research on poverty in Bengkulu Province has been conducted by [6], conducted research on poverty modeling in Bengkulu Province using Small Area Estimation and Penalized Spline Semiparametric Regression. This study produced a poverty model in Bengkulu Province, namely a linear P-Spline model with 1 knot.…”

Section: Introductionmentioning

confidence: 99%

Spatial Modeling of Poverty in Bengkulu Province With Mixed Geographically Weighted Regression

Nugroho,

Rini,

Jomecho

et al. 2024

BAREKENG: J. Math. & App.

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The percentage of poor people in Bengkulu Province is high from year to year. The poverty rate in Bengkulu Province also tends to fluctuate. If there is a decrease in the poverty rate, the decrease is relatively small. Poverty in the regions of Bengkulu Province also varies from district to district, subdistrict, and village to village, because poverty data is spatial data that varies regionally. The diversity of poverty data in Bengkulu Province is influenced by spatial effects, namely spatial dependency and spatial heterogeneity. Spatial dependency occurs due to spatial error correlation in cross section data, while spatial heterogeneity occurs due to random area effects, which is the difference between one region and another. Therefore, classical methods are not qualified enough to analyze the resulting diversity. This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. This modeling aims to identify and analyze poverty indicators in Bengkulu Province spatially, namely based on poverty data in each district/city in Bengkulu Province. The results showed that by using the MGWR method, the variables that locally influence the percentage of extreme poor people in each district/city in Bengkulu Province are Female Household Head Gender and not having a waterheater . Meanwhile, the variable that has a global effect on the percentage of the extreme poor in each district/city in Bengkulu Province is not having a flat screen television ().

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Section: Introductionmentioning

confidence: 99%

Spatial Modeling of Poverty in Bengkulu Province With Mixed Geographically Weighted Regression

Nugroho,

Rini,

Jomecho

et al. 2024

BAREKENG: J. Math. & App.

View full text Add to dashboard Cite

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“…The previous studies that applied small area estimation using P-Spline generally did not include spatial effects in the model [6][7] [8]. In addition, small area estimation research for the MYS indicator is still rarely carried out.…”

Section: Introductionmentioning

confidence: 99%

Small Area Estimation of Mean Years School in Kabupaten Bogor Using Semiparametric P-Spline

Putri¹,

Indahwati²,

Kurnia³

2022

BAREKENG: J. Math. & App.

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The Fay-Herriot model, generally uses the EBLUP (Empirical Best Linear Unbiased Prediction) method, is less flexible due to the assumption of linearity. The P-Spline semiparametric model is a modification of the Fay-Herriot model which can accommodate the presence of two components, linear and nonlinear predictors. This paper also deals spatial dependence among the random area effects so that a model with spatially autocorrelated errors will be implemented, known as the SEBLUP (Spatial Empirical Best Linear Unbiased Prediction) method. Using data from SUSENAS, PODES, and some publication from BPS, the main objective of this study is to estimate the mean years school at kecamatan level in Kabupaten Bogor using EBLUP, Semiparametric P-Spline approach and SEBLUP method. The results show that based on the RRMSE value, the cubic P-Spline model with three knots predicts the mean years school better than EBLUP. Meanwhile, the addition of spatial effects into the small area estimation has not been able to improve the estimated value of the P-Spline semiparametric approach.

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The Effect of Human Development Index on Poverty Model in Indonesia using Penalized Basis Spline Nonparametric Regression.

Hasanah

Bahri

Fitriyani

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Poverty is an issue of special concern to various countries in the world, including Indonesia. One factor that can affect poverty is the Human Development Index (HDI). The aim of this study is to model of the problem of poverty based on HDI. Using nonparametric spline regression was used with the penalized basis spline (PB-Spline) approach, which can overcome the problem of selecting many knot points and the location of knots in spline regression. This study was obtained three models, i.e. model with one knot point, model with two knot points, and model with three knot points. Based on indicators of the value of Generalized Cross Validation (GCV) and the value of Mean Square Error (MSE), the best model was a model with three knots, with smoothing parameter value of 1000, 11.26236 value of GCV and 11.08420 value of MSE.

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

Cited by 3 publications

References 6 publications

Spatial Modeling of Poverty in Bengkulu Province With Mixed Geographically Weighted Regression

Spatial Modeling of Poverty in Bengkulu Province With Mixed Geographically Weighted Regression

Small Area Estimation of Mean Years School in Kabupaten Bogor Using Semiparametric P-Spline

The Effect of Human Development Index on Poverty Model in Indonesia using Penalized Basis Spline Nonparametric Regression.

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