We modify a previously suggested GMM estimator in a spatial panel regression model by taking into account the difference between disturbances and regression residuals and derive its asymptotic properties. Simulation results and an empirical application to Indonesian rice data illustrate the improvement in finite samples. JEL Classification: C13, C21

Recently, the regularized coding-based classification methods (e.g. SRC and CRC) show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regr...

in this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of multicolinearity. if one deletes the variable (s) that is (are) responsible for multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. to improve this method, we run the deleted va...

Left: mouth against the mouth template. Right: nonmouth against the same template. Least squares regression (red) and arithmetic mean (blue).. .. .. .. 22 2.7 Left: mouth against the mouth template. Right: nonmouth against the same template. Weighted regression (red) and weighted mean (blue) with radial weights. 22 2.8 Left: the mouth of the person from Figure 1.1. Right: residuals of the linea...

Usually, empirical distribution functions are used to estimate the theoretical distribution function of known functions (X) of the observable random variable X. In practice, many researchers are using empirical distribution functions constructed from residuals, which are estimations of a non-observable error terms in linear models. This falls under a class of more general problems in which one ...

In this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of Multicolinearity. If one deletes the variable (s) that is (are) responsible for Multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. To improve this method, we run the deleted va...