The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-...The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-erful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in ef-ficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the pres-ence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be es-timated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of de-cision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a con-tinuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.展开更多
The Contingent Valuation Method is used to evaluate individual preferences for a change concerning a public non-market resource or property. The objective is to build a nonparametric forecasting model of an individual...The Contingent Valuation Method is used to evaluate individual preferences for a change concerning a public non-market resource or property. The objective is to build a nonparametric forecasting model of an individual＇s Willingness To Pay according to geographical location. Within this framework, an estimator （of type Nadaraya-Watson） is proposed for the regression of the variable related to geolocation. The specific characteristics of the location variable lead us to a more general regression model than the traditional models. Results are established for convergence of our estimator.展开更多
This paper studies nonparametric estimation of the regression function with surrogate outcome data under double-sampling designs, where a proxy response is observed for the full sample and the true response is observe...This paper studies nonparametric estimation of the regression function with surrogate outcome data under double-sampling designs, where a proxy response is observed for the full sample and the true response is observed on a validation set. A new estimation approach is proposed for estimating the regression function. The authors first estimate the regression function with a kernel smoother based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from the non-validation subsample and the surrogate of response from the full sample. Asymptotic normality of the proposed estimator is derived. The effectiveness of the proposed method is demonstrated via simulations.展开更多
Predicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals.However,while back-projection techniques allow reliable estimation of the numbers of infected i...Predicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals.However,while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past,they are less reliable in the recent past.We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic.The proposed methods are noniterative,easily computed and asymptotically normal with simple variance formulas.Simulations show that the proposed methods are much more robust and accurate than the existing back projection method,especially for the recent past,which is our primary interest.We apply the proposed methods to the 2003 Severe Acute Respiratory Syndorme(SARS) epidemic in Hong Kong.展开更多
This article proposes a simple nonparametric estimator of quantile residual lifetime function under left-truncated and right-censored data. The asymptotic consistency and normality of this estimator are proved and the...This article proposes a simple nonparametric estimator of quantile residual lifetime function under left-truncated and right-censored data. The asymptotic consistency and normality of this estimator are proved and the variance expression is calculated. Two bootstrap procedures are employed in the simulation study,where the latter bootstrap from Zeng and Lin(2008) is 4000 times faster than the former naive one, and the numerical results in both methods show that our estimating approach works well. A real data example is used to illustrate its application.展开更多
Parameter estimation of the 2 R-1 C model is usually performed using iterative methods that require high-performance processing units.Consequently,there is a strong motivation to develop less time-consuming and more p...Parameter estimation of the 2 R-1 C model is usually performed using iterative methods that require high-performance processing units.Consequently,there is a strong motivation to develop less time-consuming and more power-efficient parameter estimation methods.Such low-complexity algorithms would be suitable for implementation in portable microcontroller-based devices.In this study,we propose the quadratic interpolation non-iterative parameter estimation(QINIPE)method,based on quadratic interpolation of the imaginary part of the measured impedance,which enables more accurate estimation of the characteristic frequency.The 2 R-1 C model parameters are subsequently calculated from the real and imaginary parts of the measured impedance using a set of closed-form expressions.Comparative analysis conducted on the impedance data of the 2 R-1 C model obtained in both simulation and measurements shows that the proposed QINIPE method reduces the number of required measurement points by 80%in comparison with our previously reported non-iterative parameter estimation(NIPE)method,while keeping the relative estimation error to less than 1%for all estimated parameters.Both non-iterative methods are implemented on a microcontroller-based device;the estimation accuracy,RAM,flash memory usage,and execution time are monitored.Experiments show that the QINIPE method slightly increases the execution time by 0.576 ms(about 6.7%),and requires 24%(1.2 KB)more flash memory and just 2.4%(32 bytes)more RAM in comparison to the NIPE method.However,the impedance root mean square errors(RMSEs)of the QINIPE method are decreased to 42.8%(for the real part)and 64.5%(for the imaginary part)of the corresponding RMSEs obtained using the NIPE method.Moreover,we compared the QINIPE and the complex nonlinear least squares(CNLS)estimation of the 2 R-1 C model parameters.The results obtained show that although the estimation accuracy of the QINIPE is somewhat lower than the estimation accuracy of the CNLS,it is still satisfactory for many practical purposes and its execution time reduces to1/45–1/30.展开更多
基金Supported by the National High Technology Research and Development Program of China (2006AA04Z176)
文摘The detection and identification of gross errors, especially measurement bias, plays a vital role in data reconciliation for nonlinear dynamic systems. Although parameter estimation method has been proved to be a pow-erful tool for bias identification, without a reliable and efficient bias detection strategy, the method is limited in ef-ficiency and cannot be applied widely. In this paper, a new bias detection strategy is constructed to detect the pres-ence of measurement bias and its occurrence time. With the help of this strategy, the number of parameters to be es-timated is greatly reduced, and sequential detections and iterations are also avoided. In addition, the number of de-cision variables of the optimization model is reduced, through which the influence of the parameters estimated is reduced. By incorporating the strategy into the parameter estimation model, a new methodology named IPEBD (Improved Parameter Estimation method with Bias Detection strategy) is constructed. Simulation studies on a con-tinuous stirred tank reactor (CSTR) and the Tennessee Eastman (TE) problem show that IPEBD is efficient for eliminating random errors, measurement biases and outliers contained in dynamic process data.
文摘The Contingent Valuation Method is used to evaluate individual preferences for a change concerning a public non-market resource or property. The objective is to build a nonparametric forecasting model of an individual＇s Willingness To Pay according to geographical location. Within this framework, an estimator （of type Nadaraya-Watson） is proposed for the regression of the variable related to geolocation. The specific characteristics of the location variable lead us to a more general regression model than the traditional models. Results are established for convergence of our estimator.
基金This research is supported by the National Natural Science Foundation of the US under Grant No. DMS- 0906482.
文摘This paper studies nonparametric estimation of the regression function with surrogate outcome data under double-sampling designs, where a proxy response is observed for the full sample and the true response is observed on a validation set. A new estimation approach is proposed for estimating the regression function. The authors first estimate the regression function with a kernel smoother based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from the non-validation subsample and the surrogate of response from the full sample. Asymptotic normality of the proposed estimator is derived. The effectiveness of the proposed method is demonstrated via simulations.
基金supported in part by National Natural Science Foundation of China(Grant Nos. 10771148,11071197)supported by an RGC grant,the Chief Executive Community Project and Hong Kong Jockey Club Charities Trust
文摘Predicting the future course of an epidemic depends on being able to estimate the current numbers of infected individuals.However,while back-projection techniques allow reliable estimation of the numbers of infected individuals in the more distant past,they are less reliable in the recent past.We propose two new nonparametric methods to estimate the unobserved numbers of infected individuals in the recent past in an epidemic.The proposed methods are noniterative,easily computed and asymptotically normal with simple variance formulas.Simulations show that the proposed methods are much more robust and accurate than the existing back projection method,especially for the recent past,which is our primary interest.We apply the proposed methods to the 2003 Severe Acute Respiratory Syndorme(SARS) epidemic in Hong Kong.
基金supported by National Natural Science Foundation of China(Grant No.71271128)the State Key Program of National Natural Science Foundation of China(Grant No.71331006)+2 种基金NCMIS and Shanghai University of Finance and Economics through Project 211 Phase IVShanghai Firstclass Discipline A,Outstanding Ph D Dissertation Cultivation Funds of Shanghai University of Finance and EconomicsGraduate Education Innovation Funds of Shanghai University of Finance and Economics(Grant No.CXJJ-2011-438)
文摘This article proposes a simple nonparametric estimator of quantile residual lifetime function under left-truncated and right-censored data. The asymptotic consistency and normality of this estimator are proved and the variance expression is calculated. Two bootstrap procedures are employed in the simulation study,where the latter bootstrap from Zeng and Lin(2008) is 4000 times faster than the former naive one, and the numerical results in both methods show that our estimating approach works well. A real data example is used to illustrate its application.
基金Project supported by the Ministry of Science and Technology of the Republic of Srpska(No.19/6-020/961-143/18)the EU’s H2020 MSCA MEDLEM(No.690876).
文摘Parameter estimation of the 2 R-1 C model is usually performed using iterative methods that require high-performance processing units.Consequently,there is a strong motivation to develop less time-consuming and more power-efficient parameter estimation methods.Such low-complexity algorithms would be suitable for implementation in portable microcontroller-based devices.In this study,we propose the quadratic interpolation non-iterative parameter estimation(QINIPE)method,based on quadratic interpolation of the imaginary part of the measured impedance,which enables more accurate estimation of the characteristic frequency.The 2 R-1 C model parameters are subsequently calculated from the real and imaginary parts of the measured impedance using a set of closed-form expressions.Comparative analysis conducted on the impedance data of the 2 R-1 C model obtained in both simulation and measurements shows that the proposed QINIPE method reduces the number of required measurement points by 80%in comparison with our previously reported non-iterative parameter estimation(NIPE)method,while keeping the relative estimation error to less than 1%for all estimated parameters.Both non-iterative methods are implemented on a microcontroller-based device;the estimation accuracy,RAM,flash memory usage,and execution time are monitored.Experiments show that the QINIPE method slightly increases the execution time by 0.576 ms(about 6.7%),and requires 24%(1.2 KB)more flash memory and just 2.4%(32 bytes)more RAM in comparison to the NIPE method.However,the impedance root mean square errors(RMSEs)of the QINIPE method are decreased to 42.8%(for the real part)and 64.5%(for the imaginary part)of the corresponding RMSEs obtained using the NIPE method.Moreover,we compared the QINIPE and the complex nonlinear least squares(CNLS)estimation of the 2 R-1 C model parameters.The results obtained show that although the estimation accuracy of the QINIPE is somewhat lower than the estimation accuracy of the CNLS,it is still satisfactory for many practical purposes and its execution time reduces to1/45–1/30.
