Pemilihan Variabel Pada Model Geographically Weighted Regression

Yasin, Hasbi

doi:10.14710/medstat.4.2.63-72

Cited by 17 publications

(14 citation statements)

References 8 publications

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“…Fungsi kernel memberi nilai penimbang pada matriks W i berdasarkan kedekatan suatu titik regresi ke-i terhadap titik lokasi data di sekitar i. Kedekatan ini dihitung berdasarkan jarak yang diukur menurut koordinat masing-masing titik. Terdapat empat fungsi kernel yang umum digunakan dalam menentukan nilai penimbang tersebut, yaitu fungsi kernel Gaussian, Exponential, Bisquare, dan Tricube (Fotheringham, Brunsdon & Charlton, 2002;Nakaya et al, 2005;Yasin, 2011).…”

Section: Abstrakunclassified

Pemodelan Angka Kematian Ibu Di Indonesia Dengan Pendekatan Geographically Weighted Poisson Regression

Destyanugraha

Kurniawan

2017

j. Matematika Sains dan Teknologi

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Maternal Mortality Rate (MMR) is one of the important indicators of a country's health development and is one of the targets of achieving Sustainable Development Goals (SDGs). This study aims to develop a model on the relationship of MMR with provincial health development variables using the Geographically Weighted Poisson Regression (GWPR) method; as well as mapping the model to the provincial map. Estimation of model parameters using PODES data for 2011 and the projected health and projection profile of 2010-2013. The obtained model consists of four variables that influence the number of maternal deaths: (1) the ratio of health facilities, (2) the ratio of midwives, (3) the percentage of deliveries assisted by health personnel, and (4) the percentage of pregnant women received Fe tablets. The mapping of the four variables into the provincial map yields three groups of regions with different levels of significance of variables. The AIC value and the GWPR model deviance are lower than Poisson regression, indicated that the AKI model with GWPR is better than Poisson regression. Angka Kematian Ibu (AKI) merupakan salah satu indikator penting pembangunan kesehatan suatu negara danmenjadi salah satu target pencapaian Sustainable Development Goals (SDGs). Penelitian ini bertujuan menyusun model hubungan AKI dengan variabel-variabel pembangunan kesehatan provinsi menggunakan metode Geographically Weighted Poisson Regression (GWPR) dan memetakan model tersebut kedalam peta provinsi. Estimasi parameter model menggunakan data PODES tahun 2011 dan profil kesehatan dan proyeksi penduduk tahun 2010-2013. Model yang diperoleh terdiri dari empat variabel yang mempengaruhi jumlah kematian ibu yaitu rasio sarana kesehatan, rasio bidan, persentase persalinan ditolong tenaga kesehatan, dan persentase ibu hamil mendapat tablet Fe. Pemetaan empat variabel tersebut ke dalam peta provinsi menghasilkan tiga kelompok wilayah dengan tingkat signifikansi variabel yang berbeda-beda. Nilai AIC dan deviance model GWPR lebih rendah dari regresi Poisson menunjukkan bahwa model AKI dengan GWPR lebih baikdari regresi Poisson.

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Section: Abstrakunclassified

Pemodelan Angka Kematian Ibu Di Indonesia Dengan Pendekatan Geographically Weighted Poisson Regression

Destyanugraha

Kurniawan

2017

j. Matematika Sains dan Teknologi

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“…Terdapat empat fungsi kernel yang umum digunakan dalam menentukan nilai penimbang tersebut, yaitu fungsi kernelGaussian, Exponential, Bisquare, dan Tricube (Fotheringham et al, 2002;Nakaya et al, 2005;Yasin, 2011).…”

Section: X=unclassified

Pemodelan Angka Kematian Ibu Di Indonesia Dengan Pendekatan Geographically Weighted Poisson Regression

Destyanugraha¹,

Kurniawan²

2017

Preprint

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Maternal Mortality Rate (MMR) is one of the important indicators of a country's health development and is one of the targets of achieving Sustainable Development Goals (SDGs ABSTRAKAngka Kematian Ibu (AKI) merupakan salah satu indikator penting pembangunan kesehatan suatu negara danmenjadi salah satu target pencapaian Sustainable Development Goals (SDGs). Penelitian ini bertujuan menyusun model hubungan AKI dengan variabel-variabel pembangunan kesehatan provinsi menggunakan metode Geographically Weighted Poisson Regression (GWPR) dan memetakan model tersebut kedalam peta provinsi. Estimasi parameter model menggunakan data PODES tahun 2011 dan profil kesehatan dan proyeksi penduduk tahun 2010-2013. Model yang diperoleh terdiri dari empat variabel yang mempengaruhi jumlah kematian ibu yaitu rasio sarana kesehatan, rasio bidan, persentase persalinan ditolong tenaga kesehatan, dan persentase ibu hamil mendapat tablet Fe. Pemetaan empat variabel tersebut ke dalam peta provinsi menghasilkan tiga kelompok wilayah dengan tingkat signifikansi variabel yang berbeda-beda. Nilai AIC dan deviance model GWPR lebih rendah dari regresi Poisson menunjukkan bahwa model AKI dengan GWPR lebih baikdari regresi Poisson.Kata kunci: angka kematian ibu, GWPR, pemodelan, regresi Poisson.

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“…e author discussed spatial analysis in detail by using OLS and estimator of spatial regression models with the maximum likelihood estimation (MLE) methods. Yasin [3] proposed the GWR stepwise method in order to choose a significant variable. e selection of the stepwise GWR method reduces several predictor variables that are not significant to the response variable.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Error Variance-Covariance Parameters Using Multivariate Geographically Weighted Regression Model

Harini

2020

Abstract and Applied Analysis

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The Multivariate Geographically Weighted Regression (MGWR) model is a development of the Geographically Weighted Regression (GWR) model that takes into account spatial heterogeneity and autocorrelation error factors that are localized at each observation location. The MGWR model is assumed to be an error vector ε that distributed as a multivariate normally with zero vector mean and variance-covariance matrix Σ at each location ui,vi, which Σ is sized qxq for samples at the i-location. In this study, the estimated error variance-covariance parameters is obtained from the MGWR model using Maximum Likelihood Estimation (MLE) and Weighted Least Square (WLS) methods. The selection of the WLS method is based on the weighting function measured from the standard deviation of the distance vector between one observation location and another observation location. This test uses a statistical inference procedure by reducing the MGWR model equation so that the estimated error variance-covariance parameters meet the characteristics of unbiased. This study also provides researchers with an understanding of statistical inference procedures.

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Pemilihan Variabel Pada Model Geographically Weighted Regression

Cited by 17 publications

References 8 publications

Pemodelan Angka Kematian Ibu Di Indonesia Dengan Pendekatan Geographically Weighted Poisson Regression

Pemodelan Angka Kematian Ibu Di Indonesia Dengan Pendekatan Geographically Weighted Poisson Regression

Pemodelan Angka Kematian Ibu Di Indonesia Dengan Pendekatan Geographically Weighted Poisson Regression

Estimation of Error Variance-Covariance Parameters Using Multivariate Geographically Weighted Regression Model

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