This paper proposes the Nonnegative Garrote(NG)estimator for linear model with heteroscedastic errors.On the other hand,under some regularity conditions,the authors show the asymptotic optimality of the NG estimator b...This paper proposes the Nonnegative Garrote(NG)estimator for linear model with heteroscedastic errors.On the other hand,under some regularity conditions,the authors show the asymptotic optimality of the NG estimator by referring to the idea of the asymptotic optimality of the model average estimator.Simulation results and a real data analysis are reported for testing the results obtained previously.These results provide a stronger theoretical basis for the use of NG estimator by strengthening existing findings.展开更多
In practice,the model structure,parameters and time-delay of the actual process may vary simultaneously.However,the general identification methods of the 3 items are performed with separate procedures which is very in...In practice,the model structure,parameters and time-delay of the actual process may vary simultaneously.However,the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application.In view of the fact that variable selection procedure can ensure a compact model with robust input-output,relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure,parameters and time-delay,non-negative garrote(NNG)algorithm is introduced and applied to system identification and the corresponding procedures are presented.The application of NNG variable selection algorithm to the identification of single input single output(SISO)system,multiple input multiple output(MIN1O)system and Wood-Berry tower industry are investigated.The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square(OLS)algorithms.The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure,parameters and time-delay with high precision.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61501331the Natural Science Foundation of Zhejiang Province under Grant No.LY14F010002。
文摘This paper proposes the Nonnegative Garrote(NG)estimator for linear model with heteroscedastic errors.On the other hand,under some regularity conditions,the authors show the asymptotic optimality of the NG estimator by referring to the idea of the asymptotic optimality of the model average estimator.Simulation results and a real data analysis are reported for testing the results obtained previously.These results provide a stronger theoretical basis for the use of NG estimator by strengthening existing findings.
基金This work was supported by National Natural Science Foundation of China(No.61171145).
文摘In practice,the model structure,parameters and time-delay of the actual process may vary simultaneously.However,the general identification methods of the 3 items are performed with separate procedures which is very inconvenient in practical application.In view of the fact that variable selection procedure can ensure a compact model with robust input-output,relation and in order to explore the feasibility of variable selection algorithm for the simultaneous identification of process structure,parameters and time-delay,non-negative garrote(NNG)algorithm is introduced and applied to system identification and the corresponding procedures are presented.The application of NNG variable selection algorithm to the identification of single input single output(SISO)system,multiple input multiple output(MIN1O)system and Wood-Berry tower industry are investigated.The identification accuracy and the time-series variable selection results are analyzed and compared between NNG and ordinary least square(OLS)algorithms.The derived excellent results show that the proposed NNG-based modeling algorithm can be utilized for simultaneous identification of the model structure,parameters and time-delay with high precision.
