In this paper, we propose a log-normal linear model whose errors are first-order correlated, and suggest a two-stage method for the efficient estimation of the conditional mean of the response variable at the original...In this paper, we propose a log-normal linear model whose errors are first-order correlated, and suggest a two-stage method for the efficient estimation of the conditional mean of the response variable at the original scale. We obtain two estimators which minimize the asymptotic mean squared error （MM） and the asymptotic bias （MB）, respectively. Both the estimators are very easy to implement, and simulation studies show that they are perform better.展开更多
In the presence of multicollinearity in logistic regression, the variance of the Maximum Likelihood Estimator (MLE) becomes inflated. Siray et al. (2015) [1] proposed a restricted Liu estimator in logistic regression ...In the presence of multicollinearity in logistic regression, the variance of the Maximum Likelihood Estimator (MLE) becomes inflated. Siray et al. (2015) [1] proposed a restricted Liu estimator in logistic regression model with exact linear restrictions. However, there are some situations, where the linear restrictions are stochastic. In this paper, we propose a Stochastic Restricted Maximum Likelihood Estimator (SRMLE) for the logistic regression model with stochastic linear restrictions to overcome this issue. Moreover, a Monte Carlo simulation is conducted for comparing the performances of the MLE, Restricted Maximum Likelihood Estimator (RMLE), Ridge Type Logistic Estimator(LRE), Liu Type Logistic Estimator(LLE), and SRMLE for the logistic regression model by using Scalar Mean Squared Error (SMSE).展开更多
针对基于接收信号强度(received signal strength,RSS)测距定位框架,提出基于贝叶斯测距和迭代最小二乘定位的RSS的定位算法.在测距阶段,先利用贝叶斯概率模型处理测距过程,并采用最小均方误差(minimum mean square error,MMSE)估计距离...针对基于接收信号强度(received signal strength,RSS)测距定位框架,提出基于贝叶斯测距和迭代最小二乘定位的RSS的定位算法.在测距阶段,先利用贝叶斯概率模型处理测距过程,并采用最小均方误差(minimum mean square error,MMSE)估计距离;在定位阶段,利用迭代最小二乘(iterative least square,ILS)估计节点的位置,最后重点对其定位性能做了理论分析和对比实验.仿真结果表明,提出的MMSE+ILS定位的方案极大地提高了定位精度,并降低了计算复杂度,但运行时间略有提高.展开更多
提出V-BLAST(Vertical Bell Labs Layered Space-Time)系统的一种新的联合检测算法,先通过迫零(ZF)或最小均方误差(MMSE)算法初步检测,粗判决得到包含nT层信号的初始解向量,然后在信号解空间中找到与初始解有最小错误距离的nT个解向量,...提出V-BLAST(Vertical Bell Labs Layered Space-Time)系统的一种新的联合检测算法,先通过迫零(ZF)或最小均方误差(MMSE)算法初步检测,粗判决得到包含nT层信号的初始解向量,然后在信号解空间中找到与初始解有最小错误距离的nT个解向量,最后用最大似然(ML)算法从这nT+1个解向量中找出最优解。以QPSK调制方式为例,计算机仿真验证了在准静态平坦衰落信道中新算法的检测性能,与ZF和MMSE算法比较,该算法能明显提高系统性能,在4×4的MIMO系统中,当误码率在10-2数量级时可以获得6dB左右的增益,而且新算法比ML算法的复杂度低很多。展开更多
基金The NSF(11271155) of ChinaResearch Fund(20070183023) for the Doctoral Program of Higher Education
文摘In this paper, we propose a log-normal linear model whose errors are first-order correlated, and suggest a two-stage method for the efficient estimation of the conditional mean of the response variable at the original scale. We obtain two estimators which minimize the asymptotic mean squared error （MM） and the asymptotic bias （MB）, respectively. Both the estimators are very easy to implement, and simulation studies show that they are perform better.
文摘In the presence of multicollinearity in logistic regression, the variance of the Maximum Likelihood Estimator (MLE) becomes inflated. Siray et al. (2015) [1] proposed a restricted Liu estimator in logistic regression model with exact linear restrictions. However, there are some situations, where the linear restrictions are stochastic. In this paper, we propose a Stochastic Restricted Maximum Likelihood Estimator (SRMLE) for the logistic regression model with stochastic linear restrictions to overcome this issue. Moreover, a Monte Carlo simulation is conducted for comparing the performances of the MLE, Restricted Maximum Likelihood Estimator (RMLE), Ridge Type Logistic Estimator(LRE), Liu Type Logistic Estimator(LLE), and SRMLE for the logistic regression model by using Scalar Mean Squared Error (SMSE).
文摘针对基于接收信号强度(received signal strength,RSS)测距定位框架,提出基于贝叶斯测距和迭代最小二乘定位的RSS的定位算法.在测距阶段,先利用贝叶斯概率模型处理测距过程,并采用最小均方误差(minimum mean square error,MMSE)估计距离;在定位阶段,利用迭代最小二乘(iterative least square,ILS)估计节点的位置,最后重点对其定位性能做了理论分析和对比实验.仿真结果表明,提出的MMSE+ILS定位的方案极大地提高了定位精度,并降低了计算复杂度,但运行时间略有提高.
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.61071196)教育部新世纪优秀人才支持计划(No.NCET-10-0927)+7 种基金信号与信息处理重庆市市级重点实验室建设项目(No.CSTC2009CA2003)重庆市自然科学基金项目(No.CSTC2009BB2287No.CSTC2010BB2398No.CSTC2010BB2411)
文摘提出V-BLAST(Vertical Bell Labs Layered Space-Time)系统的一种新的联合检测算法,先通过迫零(ZF)或最小均方误差(MMSE)算法初步检测,粗判决得到包含nT层信号的初始解向量,然后在信号解空间中找到与初始解有最小错误距离的nT个解向量,最后用最大似然(ML)算法从这nT+1个解向量中找出最优解。以QPSK调制方式为例,计算机仿真验证了在准静态平坦衰落信道中新算法的检测性能,与ZF和MMSE算法比较,该算法能明显提高系统性能,在4×4的MIMO系统中,当误码率在10-2数量级时可以获得6dB左右的增益,而且新算法比ML算法的复杂度低很多。
