A kind of combining forecasting model based on the generalized weighted functional mean is proposed. Two kinds of parameter estimation methods with its weighting coefficients using the algorithm of quadratic programmi...A kind of combining forecasting model based on the generalized weighted functional mean is proposed. Two kinds of parameter estimation methods with its weighting coefficients using the algorithm of quadratic programming are given. The efficiencies of this combining forecasting model and the comparison of the two kinds of parameter estimation methods are demonstrated with an example. A conclusion is obtained, which is useful for the correct application of the above methods.展开更多
In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes...In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.展开更多
文摘A kind of combining forecasting model based on the generalized weighted functional mean is proposed. Two kinds of parameter estimation methods with its weighting coefficients using the algorithm of quadratic programming are given. The efficiencies of this combining forecasting model and the comparison of the two kinds of parameter estimation methods are demonstrated with an example. A conclusion is obtained, which is useful for the correct application of the above methods.
文摘In dealing with nonparametric regression the GAM procedure is the most versatile of several new procedures. The terminology behind this procedure is more flexible than traditional parametric modeling tools. It relaxes the usual assumptions of parametric model and enables us to uncover structure to establish the relationship between independent variables and dependent variable in exponential family that may not be obvious otherwise. In this paper, we discussed two methods of fitting generalized additive logistic regression model, one based on Newton Raphson method and another based on iterative weighted least square method for first and second order Taylor series expansion. The use of the GAM procedure with the specified set of weights, using local scoring algorithm, was applied to real life data sets. The cubic spline smoother is applied to the independent variables. Based on nonparametric regression and smoothing techniques, this procedure provides powerful tools for data analysis.
