Recently, Gijbels and Rousson<SUP>[6]</SUP> suggested a new approach, called nonparametric least-squares test, to check polynomial regression relationships. Although this test procedure is not only simple ...Recently, Gijbels and Rousson<SUP>[6]</SUP> suggested a new approach, called nonparametric least-squares test, to check polynomial regression relationships. Although this test procedure is not only simple but also powerful in most cases, there are several other parameters to be chosen in addition to the kernel and bandwidth. As shown in their paper, choice of these parameters is crucial but sometimes intractable. We propose in this paper a new statistic which is based on sample variance of the locally estimated pth derivative of the regression function at each design point. The resulting test is still simple but includes no extra parameters to be determined besides the kernel and bandwidth that are necessary for nonparametric smoothing techniques. Comparison by simulations demonstrates that our test performs as well as or even better than Gijbels and Rousson’s approach. Furthermore, a real-life data set is analyzed by our method and the results obtained are satisfactory.展开更多
Partly linear regression model is useful in practice, but littleis investigated in the literature to adapt it to the real data which are dependent and conditionally heteroscedastic. In this paper, the estimators of th...Partly linear regression model is useful in practice, but littleis investigated in the literature to adapt it to the real data which are dependent and conditionally heteroscedastic. In this paper, the estimators of the regression components are constructed via local polynomial fitting and the large sample properties are explored. Under certain mild regularities, the conditions are obtained to ensure that the estimators of the nonparametric component and its derivatives are consistent up to the convergence rates which are optimal in the i.i.d. case, and the estimator of the parametric component is root-n consistent with the same rate as for parametric model. The technique adopted in the proof differs from that used and corrects the errors in the reference by Hamilton and Truong under i.i.d. samples.展开更多
QRS detection is very important in cardiovascular disease diagnosis and ECG (electrocardiogram) monitor, because it is the precondition of the calculation of correlative parameters and diagnosis. This paper presents a...QRS detection is very important in cardiovascular disease diagnosis and ECG (electrocardiogram) monitor, because it is the precondition of the calculation of correlative parameters and diagnosis. This paper presents a non-parametric derivative-based method for R wave detection in ECG signal. This method firstly uses a digital filter to cut out noises from ECG signals, utilizes local polynomial fitting that is a non-parametric derivative-based method to estimate the derivative values, and then selects appropriate thresholds by the difference, and the algorithm adaptively adjusts the size of thresholds periodically according to the different needs. Afterwards, the position of R wave is detected by the estimation of the first-order derivative values with nonparametric local polynomial statistical model. In addition, in order to improve the accuracy of detection, the method of redundant detection and missing detection are applied in this paper. The clinical experimental data are used to evaluate the effectiveness of the algorithm. Experimental results show that the method in the process of the detection of R wave is much smoother, compared with differential threshold algorithm and it can detect the R wave in the ECG signals accurately.展开更多
We consider the problem of parameter estimation in both linear and nonlinear ordinary differential equation（ODE） models. Nonlinear ODE models are widely used in applications. But their analytic solutions are usually...We consider the problem of parameter estimation in both linear and nonlinear ordinary differential equation（ODE） models. Nonlinear ODE models are widely used in applications. But their analytic solutions are usually not available. Thus regular methods usually depend on repetitive use of numerical solutions which bring huge computational cost. We proposed a new two-stage approach which includes a smoothing method（kernel smoothing or local polynomial fitting） in the first stage, and a numerical discretization method（Eulers discretization method, the trapezoidal discretization method,or the Runge–Kutta discretization method） in the second stage. Through numerical simulations, we find the proposed method gains a proper balance between estimation accuracy and computational cost.Asymptotic properties are also presented, which show the consistency and asymptotic normality of estimators under some mild conditions. The proposed method is compared to existing methods in term of accuracy and computational cost. The simulation results show that the estimators with local linear smoothing in the first stage and trapezoidal discretization in the second stage have the lowest average relative errors. We apply the proposed method to HIV dynamics data to illustrate the practicability of the estimator.展开更多
In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models （GLMs）, when designs are fixed points and random variables respect...In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models （GLMs）, when designs are fixed points and random variables respectively, Bias-corrected confidence bands are proposed for the （conditional） variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the （conditional） variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparamctric autoregressive times series model with heteroscedastic conditional variance.展开更多
目的准确的解缠绕相位是两点或三点Dixon技术等在磁共振临床应用的前提和关键,然而当相位图像中存在严重噪声、快速相位变换或不连通区域时,当前许多已经提出的相位解缠绕算法将会失败。为此本文提出一种基于相位分区和局部多项式曲面...目的准确的解缠绕相位是两点或三点Dixon技术等在磁共振临床应用的前提和关键,然而当相位图像中存在严重噪声、快速相位变换或不连通区域时,当前许多已经提出的相位解缠绕算法将会失败。为此本文提出一种基于相位分区和局部多项式曲面拟合的相位解缠绕新方法,该新方法在相位图像存在严重噪声、快速相位变换或不连通区域的情况下仍可以稳定可靠的工作。方法首先将获得的相位图像分成连通块,块内相位都在给定的相位区间内,把像素个数小于给定阈值的块归类为残余像素。然后利用局域增长的局部多项式曲面拟合方法依次进行块与块之间相位解缠绕和残余像素相位解缠绕。最后使用仿真与真实磁共振Dixon数据来评价提出方法,并与PRELUDE(phase region expanding labeler for unwrapping discrete estimates)方法进行了比较。结果在不同信噪比、快速相位变换或存在不连通区域的仿真实验中,即使当数据中存在信噪比为0.5、相邻相位变换大于π弧度或不连通区域时,提出方法的平均错误率不大于0.51%。对于100层真实的磁共振膝关节和踝关节水与脂肪分离图像,提出方法生成结果中发生明显解缠绕错误及水与脂肪互换的比率为6.00%,而PRELUDE却为42.00%。结论本文提出了一种磁共振相位解缠绕算法,利用相位分区方法,可靠的实现相位图像分块;利用局部多项式曲面拟合方法,准确的实现相位解缠绕。提出方法能够更加鲁棒的实现相位解缠绕,这将有益于相位相关的磁共振临床的应用,如两点和三点Dixon水脂分离技术、磁敏感加权成像和人脑相位成像等。展开更多
基金the National Natural Science Foundations of China (No.19971006 and 60075001).
文摘Recently, Gijbels and Rousson<SUP>[6]</SUP> suggested a new approach, called nonparametric least-squares test, to check polynomial regression relationships. Although this test procedure is not only simple but also powerful in most cases, there are several other parameters to be chosen in addition to the kernel and bandwidth. As shown in their paper, choice of these parameters is crucial but sometimes intractable. We propose in this paper a new statistic which is based on sample variance of the locally estimated pth derivative of the regression function at each design point. The resulting test is still simple but includes no extra parameters to be determined besides the kernel and bandwidth that are necessary for nonparametric smoothing techniques. Comparison by simulations demonstrates that our test performs as well as or even better than Gijbels and Rousson’s approach. Furthermore, a real-life data set is analyzed by our method and the results obtained are satisfactory.
基金This work was partially supported by the National Natural Science Foundation of China (Grant No.79930900) the Belgian Government's "Projet d'Actions de Recherche Concertees" (PARC No. 93/98-164) China Educational Ministry's Research Fund for Retur
文摘Partly linear regression model is useful in practice, but littleis investigated in the literature to adapt it to the real data which are dependent and conditionally heteroscedastic. In this paper, the estimators of the regression components are constructed via local polynomial fitting and the large sample properties are explored. Under certain mild regularities, the conditions are obtained to ensure that the estimators of the nonparametric component and its derivatives are consistent up to the convergence rates which are optimal in the i.i.d. case, and the estimator of the parametric component is root-n consistent with the same rate as for parametric model. The technique adopted in the proof differs from that used and corrects the errors in the reference by Hamilton and Truong under i.i.d. samples.
文摘QRS detection is very important in cardiovascular disease diagnosis and ECG (electrocardiogram) monitor, because it is the precondition of the calculation of correlative parameters and diagnosis. This paper presents a non-parametric derivative-based method for R wave detection in ECG signal. This method firstly uses a digital filter to cut out noises from ECG signals, utilizes local polynomial fitting that is a non-parametric derivative-based method to estimate the derivative values, and then selects appropriate thresholds by the difference, and the algorithm adaptively adjusts the size of thresholds periodically according to the different needs. Afterwards, the position of R wave is detected by the estimation of the first-order derivative values with nonparametric local polynomial statistical model. In addition, in order to improve the accuracy of detection, the method of redundant detection and missing detection are applied in this paper. The clinical experimental data are used to evaluate the effectiveness of the algorithm. Experimental results show that the method in the process of the detection of R wave is much smoother, compared with differential threshold algorithm and it can detect the R wave in the ECG signals accurately.
基金Supported by NSFC(Grant Nos.11201317,11028103,11231010,11471223)Doctoral Fund of Ministry of Education of China(Grant No.20111108120002)+1 种基金the Beijing Municipal Education Commission Foundation(Grant No.KM201210028005)the Key Project of Beijing Municipal Educational Commission
文摘We consider the problem of parameter estimation in both linear and nonlinear ordinary differential equation（ODE） models. Nonlinear ODE models are widely used in applications. But their analytic solutions are usually not available. Thus regular methods usually depend on repetitive use of numerical solutions which bring huge computational cost. We proposed a new two-stage approach which includes a smoothing method（kernel smoothing or local polynomial fitting） in the first stage, and a numerical discretization method（Eulers discretization method, the trapezoidal discretization method,or the Runge–Kutta discretization method） in the second stage. Through numerical simulations, we find the proposed method gains a proper balance between estimation accuracy and computational cost.Asymptotic properties are also presented, which show the consistency and asymptotic normality of estimators under some mild conditions. The proposed method is compared to existing methods in term of accuracy and computational cost. The simulation results show that the estimators with local linear smoothing in the first stage and trapezoidal discretization in the second stage have the lowest average relative errors. We apply the proposed method to HIV dynamics data to illustrate the practicability of the estimator.
基金Supported by the National Natural Science Foundation of China （No.10471140）.
文摘In this paper we introduce an appealing nonparametric method for estimating variance and conditional variance functions in generalized linear models （GLMs）, when designs are fixed points and random variables respectively, Bias-corrected confidence bands are proposed for the （conditional） variance by local linear smoothers. Nonparametric techniques are developed in deriving the bias-corrected confidence intervals of the （conditional） variance. The asymptotic distribution of the proposed estimator is established and show that the bias-corrected confidence bands asymptotically have the correct coverage properties. A small simulation is performed when unknown regression parameter is estimated by nonparametric quasi-likelihood. The results are also applicable to nonparamctric autoregressive times series model with heteroscedastic conditional variance.
文摘目的准确的解缠绕相位是两点或三点Dixon技术等在磁共振临床应用的前提和关键,然而当相位图像中存在严重噪声、快速相位变换或不连通区域时,当前许多已经提出的相位解缠绕算法将会失败。为此本文提出一种基于相位分区和局部多项式曲面拟合的相位解缠绕新方法,该新方法在相位图像存在严重噪声、快速相位变换或不连通区域的情况下仍可以稳定可靠的工作。方法首先将获得的相位图像分成连通块,块内相位都在给定的相位区间内,把像素个数小于给定阈值的块归类为残余像素。然后利用局域增长的局部多项式曲面拟合方法依次进行块与块之间相位解缠绕和残余像素相位解缠绕。最后使用仿真与真实磁共振Dixon数据来评价提出方法,并与PRELUDE(phase region expanding labeler for unwrapping discrete estimates)方法进行了比较。结果在不同信噪比、快速相位变换或存在不连通区域的仿真实验中,即使当数据中存在信噪比为0.5、相邻相位变换大于π弧度或不连通区域时,提出方法的平均错误率不大于0.51%。对于100层真实的磁共振膝关节和踝关节水与脂肪分离图像,提出方法生成结果中发生明显解缠绕错误及水与脂肪互换的比率为6.00%,而PRELUDE却为42.00%。结论本文提出了一种磁共振相位解缠绕算法,利用相位分区方法,可靠的实现相位图像分块;利用局部多项式曲面拟合方法,准确的实现相位解缠绕。提出方法能够更加鲁棒的实现相位解缠绕,这将有益于相位相关的磁共振临床的应用,如两点和三点Dixon水脂分离技术、磁敏感加权成像和人脑相位成像等。
