Estimating the Parameters Geographically Weighted Regression (GWR) with Measurement Error

Ida Mariati Hutabarat; Asep Saefuddin; Anik Djuraidah; I Wayan Mangku

doi:10.4236/ojs.2013.36049

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Estimating the Parameters Geographically Weighted Regression (GWR) with Measurement Error

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DOI: 10.4236/ojs.2013.36049 3,015 Downloads 4,580 Views Citations

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Ida Mariati Hutabarat, Asep Saefuddin, Anik Djuraidah, I Wayan Mangku

Affiliation(s)

Departement of Mathematics, Bogor Agricultural University, Bogor, Indonesia.
Departement of Mathematics, Cenderawasih University, Jayapura, Indonesia;Departement of Statistics, Bogor Agricultural University, Bogor, Indonesia.
Departement of Statistics, Bogor Agricultural University, Bogor, Indonesia.

ABSTRACT

Geographically weighted regression models with the measurement error are a modeling method that combines the global regression models with the measurement error and the weighted regression model. The assumptions used in this model are a normally distributed error with that the expectation value is zero and the variance is constant. The purpose of this study is to estimate the parameters of the model and find the properties of these estimators. Estimation is done by using the Weighted Least Squares (WLS) which gives different weighting to each location. The variance of the measurement error is known. Estimators obtained are . The properties of the estimator are unbiased and have a minimum variance.

KEYWORDS

Geographical Weighted Regression; Measurement Error; Instrumental Variable; Weighted Least Squares

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

I. Hutabarat, A. Saefuddin, A. Djuraidah and I. Mangku, "Estimating the Parameters Geographically Weighted Regression (GWR) with Measurement Error," Open Journal of Statistics, Vol. 3 No. 6, 2013, pp. 417-421. doi: 10.4236/ojs.2013.36049.

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