We consider the estimation of nonparametric regression models with predictors being measured with a mixture of Berkson and classical errors. In practice, the Berkson error arises when the variable X of interest is uno...We consider the estimation of nonparametric regression models with predictors being measured with a mixture of Berkson and classical errors. In practice, the Berkson error arises when the variable X of interest is unobservable and only a proxy of X can be measured while the inaccuracy related to the observation of the proxy causes an error of classical type. In this paper, we propose two nonparametric estimators of the regression function in the presence of either or both types of errors. We prove the asymptotic normality of our estimators and derive their rates of convergence. The finite-sample properties of the estimators are investigated through simulation studies.展开更多
A new mixed scheme which combines the variation of constants and the H1-Galerkin mixed finite element method is constructed for nonlinear Sobolev equation with nonlinear con- vection term. Optimal error estimates are ...A new mixed scheme which combines the variation of constants and the H1-Galerkin mixed finite element method is constructed for nonlinear Sobolev equation with nonlinear con- vection term. Optimal error estimates are derived for both semidiscrete and fully discrete schemes. Finally, some numerical results are given to confirm the theoretical analysis of the proposed method.展开更多
In this paper,we investigate the error estimates of mixed finite element methods for optimal control problems governed by general elliptic equations.The state and co-state are approximated by the lowest order Raviart-...In this paper,we investigate the error estimates of mixed finite element methods for optimal control problems governed by general elliptic equations.The state and co-state are approximated by the lowest order Raviart-Thomas mixed finite element spaces and the control variable is approximated by piecewise constant functions.We derive L2 and H−1-error estimates both for the control variable and the state variables.Finally,a numerical example is given to demonstrate the theoretical results.展开更多
Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary ...Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.展开更多
文摘We consider the estimation of nonparametric regression models with predictors being measured with a mixture of Berkson and classical errors. In practice, the Berkson error arises when the variable X of interest is unobservable and only a proxy of X can be measured while the inaccuracy related to the observation of the proxy causes an error of classical type. In this paper, we propose two nonparametric estimators of the regression function in the presence of either or both types of errors. We prove the asymptotic normality of our estimators and derive their rates of convergence. The finite-sample properties of the estimators are investigated through simulation studies.
基金Supported by National Natural Science Fund of China (11061021)Key Project of Chinese Ministry of Education (12024)+2 种基金Natural Science Fund of Inner Mongolia Autonomous Region (2012MS0108,2012MS0106,2011BS0102)Scientific Research Projection of Higher Schools of Inner Mongolia (NJZZ12011,NJZY13199)Program of Higher-level talents of Inner Mongolia University (125119,Z200901004,30105-125132)
文摘A new mixed scheme which combines the variation of constants and the H1-Galerkin mixed finite element method is constructed for nonlinear Sobolev equation with nonlinear con- vection term. Optimal error estimates are derived for both semidiscrete and fully discrete schemes. Finally, some numerical results are given to confirm the theoretical analysis of the proposed method.
基金supported by National Natural Science Foundation of China(Grant No.11526036)Scientific and Technological Developing Scheme of Jilin Province(Grant No.20160520108JH).
文摘In this paper,we investigate the error estimates of mixed finite element methods for optimal control problems governed by general elliptic equations.The state and co-state are approximated by the lowest order Raviart-Thomas mixed finite element spaces and the control variable is approximated by piecewise constant functions.We derive L2 and H−1-error estimates both for the control variable and the state variables.Finally,a numerical example is given to demonstrate the theoretical results.
基金supported by Science and Technology Project of State Grid Corporation of China(State Grid Jiangsu Electric Power Research Institute Power Coordinated Control Technology Research Service for Energy Storage and New Energy Power Stations in the Black Start Process,Contract Number:SGJSDK00XTJS2000357).
文摘Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.
