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Estimating the Parameters Geographically Weighted Regression (GWR) with Measurement Error
Departement of Mathematics, Cenderawasih University, Jayapura, Indonesia;Departement of Statistics, Bogor Agricultural University, Bogor, Indonesia
Departement of Statistics, Bogor Agricultural University, Bogor, Indonesia
Departement of Statistics, Bogor Agricultural University, Bogor, Indonesia
Departement of Mathematics, Bogor Agricultural University, Bogor, Indonesia
- 1 Departement of Mathematics, Cenderawasih University, Jayapura, Indonesia;Departement of Statistics, Bogor Agricultural University, Bogor, Indonesia
- 2 Departement of Statistics, Bogor Agricultural University, Bogor, Indonesia
- 3 Departement of Statistics, Bogor Agricultural University, Bogor, Indonesia
- 4 Departement of Mathematics, Bogor Agricultural University, Bogor, Indonesia
Open Journal of Statistics·Volume 03 (2013)·Pages 417–421·Published 27 December 2013·DOI10.4236/ojs.2013.36049
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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.
KeywordsGeographical Weighted RegressionMeasurement ErrorInstrumental VariableWeighted Least Squares
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