In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function...In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> presented</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.展开更多
针对受非零均值高斯噪声干扰的双率Hammerstein输出误差系统,提出一种基于偏差补偿的递推最小二乘(bias compensation based recursive least squares,BCRLS)辨识算法。首先,利用多项式变换技术将目标系统转换为可直接采用双率采样数据...针对受非零均值高斯噪声干扰的双率Hammerstein输出误差系统,提出一种基于偏差补偿的递推最小二乘(bias compensation based recursive least squares,BCRLS)辨识算法。首先,利用多项式变换技术将目标系统转换为可直接采用双率采样数据进行辨识的模型,并利用递推最小二乘(recursive least squares,RLS)算法进行辨识。其次,为了对RLS算法给出的有偏参数估计进行有效补偿,在偏差补偿原理的基础上,通过引入非奇异矩阵和扩展信息向量求解偏差项中的参数,推导得到BCRLS辨识算法。最后,通过数值仿真实验表明,BCRLS算法能够获得双率Hammerstein输出误差系统的无偏参数估计;且具有较强的鲁棒性,其辨识精度不容易受到噪声均值和方差变化的影响。展开更多
实际工程中,常常需要在非消声室环境下准确测试扬声器在自由场响应.常用的非消声室测量方法有脉冲FFT技术,时延谱技术和最长序列快速哈德曼变换技术等,但其测量精度都不是很高,尤其是对于低频测量.George and Francosi(2003)[1]WESPACV...实际工程中,常常需要在非消声室环境下准确测试扬声器在自由场响应.常用的非消声室测量方法有脉冲FFT技术,时延谱技术和最长序列快速哈德曼变换技术等,但其测量精度都不是很高,尤其是对于低频测量.George and Francosi(2003)[1]WESPACVIII上介绍了一种不使用消声室的扬声器自由场响应的测量技术,即相干平均法,其能够在普通房间里很准确地得到扬声器的自由场响应.其原理在普通房间不同位置测得扬声器到话筒的传递函数,由于每次直达声是不变的而反射声是不同的,因此把得到不同位置的频率响应求平均值就能得到接近在自由场测得的扬声器频率响应.事实上应用这种测量方法也存在一定的偏差,将探讨这种测量方法的偏差大小及其影响因素.通过对只有一个反射面的情况进行分析,得到了在不同频率时的偏差,并给出了测量的低频极限.结论是该方法在低频下,偏差会很大,而且随着频率增加偏差会变小.同时也对此平均法作了一点改进,从而在相同测量次数的情况下,能够有效减小其低频误差,从而更加准确得到扬声器的频率响应.展开更多
文摘In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators </span><span style="font-family:Verdana;">is</span><span style="font-family:Verdana;"> presented</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.
文摘针对受非零均值高斯噪声干扰的双率Hammerstein输出误差系统,提出一种基于偏差补偿的递推最小二乘(bias compensation based recursive least squares,BCRLS)辨识算法。首先,利用多项式变换技术将目标系统转换为可直接采用双率采样数据进行辨识的模型,并利用递推最小二乘(recursive least squares,RLS)算法进行辨识。其次,为了对RLS算法给出的有偏参数估计进行有效补偿,在偏差补偿原理的基础上,通过引入非奇异矩阵和扩展信息向量求解偏差项中的参数,推导得到BCRLS辨识算法。最后,通过数值仿真实验表明,BCRLS算法能够获得双率Hammerstein输出误差系统的无偏参数估计;且具有较强的鲁棒性,其辨识精度不容易受到噪声均值和方差变化的影响。
文摘实际工程中,常常需要在非消声室环境下准确测试扬声器在自由场响应.常用的非消声室测量方法有脉冲FFT技术,时延谱技术和最长序列快速哈德曼变换技术等,但其测量精度都不是很高,尤其是对于低频测量.George and Francosi(2003)[1]WESPACVIII上介绍了一种不使用消声室的扬声器自由场响应的测量技术,即相干平均法,其能够在普通房间里很准确地得到扬声器的自由场响应.其原理在普通房间不同位置测得扬声器到话筒的传递函数,由于每次直达声是不变的而反射声是不同的,因此把得到不同位置的频率响应求平均值就能得到接近在自由场测得的扬声器频率响应.事实上应用这种测量方法也存在一定的偏差,将探讨这种测量方法的偏差大小及其影响因素.通过对只有一个反射面的情况进行分析,得到了在不同频率时的偏差,并给出了测量的低频极限.结论是该方法在低频下,偏差会很大,而且随着频率增加偏差会变小.同时也对此平均法作了一点改进,从而在相同测量次数的情况下,能够有效减小其低频误差,从而更加准确得到扬声器的频率响应.
