Before revision:  Under a given structure of dependence and assumption of stationarity, based on a weight matrixcontrolled by euclidean distance function with scale parameters , we analyze the leading terms of conditional bias, variance of locally weighted pool least square estimationin each location .
After revision:  Under a given structure of dependence and assumption of stationarity, based on a weight matrixcontrolled by euclidean distance function with scale parameters , we analyze the local mean squared properties of without any assumption of the form of coefficient functions and compare it with MLWE. According to the theoretical and simulation results, geographically-weighted locally linear estimation(GWLE) is asymptotically more efficient than MLWE .