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

DOI: 10.4236/ojs.2013.36049   PDF   HTML   XML   3,388 Downloads   4,871 Views   Citations


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.

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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.

Conflicts of Interest

The authors declare no conflicts of interest.


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