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
Let two separate surveys collect related information on a single population U. Consider situation where we want to best combine data from the two surveys to yield a single set of estimates of a population quantity (po...Let two separate surveys collect related information on a single population U. Consider situation where we want to best combine data from the two surveys to yield a single set of estimates of a population quantity (population parameter) of interest. This Article presents a multiplicative bias reduction estimator for nonparametric regression to two sample problem in sample survey. The approach consists to apply a multiplicative bias correction to an estimator. The multiplicative bias correction method which was proposed, by Linton & Nielsen, 1994, assures a positive estimate and reduces the bias of the estimate with negligible increase in variance. Even as we apply this method to the two sample problem in sample survey, we found out through the study of it asymptotic properties that it was asymptotically unbiased, and statistically consistent. Furthermore an empirical study was carried out to compare the performance of the developed estimator with the existing ones.展开更多
The best-effort internet has inherent limitations on the end-to-end performance for interactive multimedia communications. This paper presents a multiple description coding （MDC） and forward error correction （FEC）...The best-effort internet has inherent limitations on the end-to-end performance for interactive multimedia communications. This paper presents a multiple description coding （MDC） and forward error correction （FEC） based multiple path transmission schemes for interactive multimedia （M3FEC）, which improves the end users’ experience by maximizing a rate-distortion （R-D） based optimization problem. The proposed model considers both the network diversity and the application’s stringent requirements, and combines the individual merits of the three promising technologies of multiple path overlay routing, MDC and FEC. Extensive numerical analysis and PlanetLab experiments demonstrate that M3FEC successfully combats packet losses, error propagation, and unpredictable network dynamics. This method also significantly increases distortion for interactive multimedia by over 10 dB than traditional IP-layer single path transmission in poor network environments, and outperforms the performance achieved by using MDC or FEC alone.展开更多
低速率拒绝服务LDoS(Low-rate Denial of Service)是一种新型的面向TCP协议的DoS攻击方式.LDoS攻击的平均流量仅占正常流量的10-20%,具有明显的周期性小信号特征,隐蔽性强.因此,检测LDoS攻击成为网络安全研究的一个难点.本文采用数字信...低速率拒绝服务LDoS(Low-rate Denial of Service)是一种新型的面向TCP协议的DoS攻击方式.LDoS攻击的平均流量仅占正常流量的10-20%,具有明显的周期性小信号特征,隐蔽性强.因此,检测LDoS攻击成为网络安全研究的一个难点.本文采用数字信号处理DSP技术,基于小信号检测理论,提出一种基于小信号模型的LDoS攻击检测的方法.该方法通过构造特征值估算矩阵,对30秒时间内(3000个采样点)到达的数据包个数进行统计;将统计值与设定的判决特征值门限比较,作为判断有无LDoS攻击的依据.如果判定成立,则通过特征值估算矩阵可较精确地计算出LDoS攻击的周期值.在NS-2环境中的仿真实验结果表明本文方法具有较高的LDoS攻击检测率.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Let two separate surveys collect related information on a single population U. Consider situation where we want to best combine data from the two surveys to yield a single set of estimates of a population quantity (population parameter) of interest. This Article presents a multiplicative bias reduction estimator for nonparametric regression to two sample problem in sample survey. The approach consists to apply a multiplicative bias correction to an estimator. The multiplicative bias correction method which was proposed, by Linton & Nielsen, 1994, assures a positive estimate and reduces the bias of the estimate with negligible increase in variance. Even as we apply this method to the two sample problem in sample survey, we found out through the study of it asymptotic properties that it was asymptotically unbiased, and statistically consistent. Furthermore an empirical study was carried out to compare the performance of the developed estimator with the existing ones.
基金Supported by the National Natural Science Foundation of China(No.90718040)NEC Laboratories China (No.LC-2008-055)
文摘The best-effort internet has inherent limitations on the end-to-end performance for interactive multimedia communications. This paper presents a multiple description coding （MDC） and forward error correction （FEC） based multiple path transmission schemes for interactive multimedia （M3FEC）, which improves the end users’ experience by maximizing a rate-distortion （R-D） based optimization problem. The proposed model considers both the network diversity and the application’s stringent requirements, and combines the individual merits of the three promising technologies of multiple path overlay routing, MDC and FEC. Extensive numerical analysis and PlanetLab experiments demonstrate that M3FEC successfully combats packet losses, error propagation, and unpredictable network dynamics. This method also significantly increases distortion for interactive multimedia by over 10 dB than traditional IP-layer single path transmission in poor network environments, and outperforms the performance achieved by using MDC or FEC alone.
文摘低速率拒绝服务LDoS(Low-rate Denial of Service)是一种新型的面向TCP协议的DoS攻击方式.LDoS攻击的平均流量仅占正常流量的10-20%,具有明显的周期性小信号特征,隐蔽性强.因此,检测LDoS攻击成为网络安全研究的一个难点.本文采用数字信号处理DSP技术,基于小信号检测理论,提出一种基于小信号模型的LDoS攻击检测的方法.该方法通过构造特征值估算矩阵,对30秒时间内(3000个采样点)到达的数据包个数进行统计;将统计值与设定的判决特征值门限比较,作为判断有无LDoS攻击的依据.如果判定成立,则通过特征值估算矩阵可较精确地计算出LDoS攻击的周期值.在NS-2环境中的仿真实验结果表明本文方法具有较高的LDoS攻击检测率.
