In this paper, a varying-coefficient density-ratio model for case-control studies is developed. We investigate the local empirical likelihood diagnosis of varying coefficient density-ratio model for case-control data....In this paper, a varying-coefficient density-ratio model for case-control studies is developed. We investigate the local empirical likelihood diagnosis of varying coefficient density-ratio model for case-control data. The local empirical log-likelihood ratios for the nonparametric coefficient functions are introduced. First, the estimation equations based on empirical likelihood method are established. Then, a few of diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation studies.展开更多
In this paper, we exploit the concept of data fusion in hybrid localization systems by combining different TOA (Time of Arrival) observables coming from different RATs (Radio Access Technology) and characterized by di...In this paper, we exploit the concept of data fusion in hybrid localization systems by combining different TOA (Time of Arrival) observables coming from different RATs (Radio Access Technology) and characterized by different precisions in order to enhance the positioning accuracy. A new Maximum Likelihood estimator is developed to fuse different measured ranges with different variances. In order to evaluate this estimator, Monte Carlo simulations are carried out in a generic environment and Cramer Rao Lower Bounds (CRLB) are investigated. This algorithm shows enhanced positioning accuracy at reasonable noise levels comparing to the typical Weighted Least Square estimator. The CRLB reveals that the choice of the number, and the configuration of Anchor nodes, and the type of RAT may enhance positioning accuracy.展开更多
In this paper, we extend the generalized likelihood ratio test to the varying-coefficient models with censored data. We investigate the asymptotic behavior of the proposed test and demonstrate that its limiting null d...In this paper, we extend the generalized likelihood ratio test to the varying-coefficient models with censored data. We investigate the asymptotic behavior of the proposed test and demonstrate that its limiting null distribution follows a distribution, with the scale constant and the number of degree of freedom being independent of nuisance parameters or functions, which is called the wilks phenomenon. Both simulated and real data examples are given to illustrate the performance of the testing approach.展开更多
针对室内移动机器人自定位算法定位精度不高、定位误差存在波动的问题,提出了一种RTFL(RFID tag floor based localization)定位算法与RSSI定位算法相结合的室内移动机器人自定位方法。由RTFL定位算法给定机器人位置估算初值和机器人所...针对室内移动机器人自定位算法定位精度不高、定位误差存在波动的问题,提出了一种RTFL(RFID tag floor based localization)定位算法与RSSI定位算法相结合的室内移动机器人自定位方法。由RTFL定位算法给定机器人位置估算初值和机器人所在的范围,通过基于RSSI的机器人自定位系统进行机器人位置的进一步精确定位。求解过程中,通过遗传算法求解极大似然方程组,并提出染色体的筛选和剔除策略。仿真实验结果表明,该方法在有效的时间内完成定位,平均定位误差为0.157 2 m,与传统的改进方法 0.332 14 m的定位误差相比,降低了近一倍。并且新方法受环境影响较小,鲁棒性较好,能够很好地满足室内移动机器人的定位要求。展开更多
随着无线传感器网络的发展,日益需要更加精确的位置信息来支撑其相关的应用。通过分析待定位节点定位过程中产生的误差,对二阶段定位算法、接收信号强度指示(received signal strength indicator,RSSI)定位技术和质心算法进行深入的研究...随着无线传感器网络的发展,日益需要更加精确的位置信息来支撑其相关的应用。通过分析待定位节点定位过程中产生的误差,对二阶段定位算法、接收信号强度指示(received signal strength indicator,RSSI)定位技术和质心算法进行深入的研究,提出了极大似然与加权质心混合定位算法:首先通过极大似然估计法对待定位节点进行粗略估计,然后利用加权质心算法对待定位节点坐标估计求精,进一步提高定位精度。仿真实验结果表明,该算法能够在定位精度方面有较大的提高。展开更多
文摘In this paper, a varying-coefficient density-ratio model for case-control studies is developed. We investigate the local empirical likelihood diagnosis of varying coefficient density-ratio model for case-control data. The local empirical log-likelihood ratios for the nonparametric coefficient functions are introduced. First, the estimation equations based on empirical likelihood method are established. Then, a few of diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation studies.
文摘In this paper, we exploit the concept of data fusion in hybrid localization systems by combining different TOA (Time of Arrival) observables coming from different RATs (Radio Access Technology) and characterized by different precisions in order to enhance the positioning accuracy. A new Maximum Likelihood estimator is developed to fuse different measured ranges with different variances. In order to evaluate this estimator, Monte Carlo simulations are carried out in a generic environment and Cramer Rao Lower Bounds (CRLB) are investigated. This algorithm shows enhanced positioning accuracy at reasonable noise levels comparing to the typical Weighted Least Square estimator. The CRLB reveals that the choice of the number, and the configuration of Anchor nodes, and the type of RAT may enhance positioning accuracy.
文摘In this paper, we extend the generalized likelihood ratio test to the varying-coefficient models with censored data. We investigate the asymptotic behavior of the proposed test and demonstrate that its limiting null distribution follows a distribution, with the scale constant and the number of degree of freedom being independent of nuisance parameters or functions, which is called the wilks phenomenon. Both simulated and real data examples are given to illustrate the performance of the testing approach.
文摘针对室内移动机器人自定位算法定位精度不高、定位误差存在波动的问题,提出了一种RTFL(RFID tag floor based localization)定位算法与RSSI定位算法相结合的室内移动机器人自定位方法。由RTFL定位算法给定机器人位置估算初值和机器人所在的范围,通过基于RSSI的机器人自定位系统进行机器人位置的进一步精确定位。求解过程中,通过遗传算法求解极大似然方程组,并提出染色体的筛选和剔除策略。仿真实验结果表明,该方法在有效的时间内完成定位,平均定位误差为0.157 2 m,与传统的改进方法 0.332 14 m的定位误差相比,降低了近一倍。并且新方法受环境影响较小,鲁棒性较好,能够很好地满足室内移动机器人的定位要求。
文摘随着无线传感器网络的发展,日益需要更加精确的位置信息来支撑其相关的应用。通过分析待定位节点定位过程中产生的误差,对二阶段定位算法、接收信号强度指示(received signal strength indicator,RSSI)定位技术和质心算法进行深入的研究,提出了极大似然与加权质心混合定位算法:首先通过极大似然估计法对待定位节点进行粗略估计,然后利用加权质心算法对待定位节点坐标估计求精,进一步提高定位精度。仿真实验结果表明,该算法能够在定位精度方面有较大的提高。
