In this paper,some problems of regression analysis in the meteorological application are discussed and main reasons for statistical inference failures are analysed.We may find the failure problems with diagnos- tic me...In this paper,some problems of regression analysis in the meteorological application are discussed and main reasons for statistical inference failures are analysed.We may find the failure problems with diagnos- tic method and solve them by different treatment.It has been proved that the treatment make the accuracy and stability of forecasting improved greatly.展开更多
In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous co...In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous computational load. To overcome this, an inverse regressionmethod is proposed. In this paper, we discuss and develop this method. To seek the interestingprojective direction, the minimization of the residual sum of squares is used as a criterion, andspline functions are applied to approximate the general nonlinear transform function. The algo-rithm is simple, and saves the computational load. Under certain proper conditions, consistencyof the estimators of the interesting direction is shown.展开更多
文摘In this paper,some problems of regression analysis in the meteorological application are discussed and main reasons for statistical inference failures are analysed.We may find the failure problems with diagnos- tic method and solve them by different treatment.It has been proved that the treatment make the accuracy and stability of forecasting improved greatly.
基金This project is supported by the National Natural Science Foundation of China
文摘In order to explore the nonlinear structure hidden in high-dimensional data, some dimen-sion reduction techniques have been developed, such as the Projection Pursuit technique (PP).However, PP will involve enormous computational load. To overcome this, an inverse regressionmethod is proposed. In this paper, we discuss and develop this method. To seek the interestingprojective direction, the minimization of the residual sum of squares is used as a criterion, andspline functions are applied to approximate the general nonlinear transform function. The algo-rithm is simple, and saves the computational load. Under certain proper conditions, consistencyof the estimators of the interesting direction is shown.
