目的:本文介绍等级结构保留失效时间模型(rank preserving structural failure time models,RPSFT)和BW法(the method of Branson and Whitehead)在临床试验转组研究中的应用,并对两种方法进行比较。方法:以部分对照组受试者在试验中途...目的:本文介绍等级结构保留失效时间模型(rank preserving structural failure time models,RPSFT)和BW法(the method of Branson and Whitehead)在临床试验转组研究中的应用,并对两种方法进行比较。方法:以部分对照组受试者在试验中途转至试验组为例,采用计算机模拟试验,查看不同截尾率、转组率水平下,RPSFT法和BW法估计试验药疗效的效果。以及探讨与传统的ITT法相比,RPSFT法和BW法的检验效能和Ⅰ类错误控制情况。结果:RPSFT法和BW法估计试验药疗效的准确性高,与传统的分析方法比较,引入的偏倚小。随着转组率和截尾率增大,RPSFT法与BW法的偏倚逐渐增大,估计的试验药疗效低于真实疗效,但RPSFT法的偏倚比BW法小。两种方法估计的均方误差(MSE)十分接近。当截尾率较大(40%)时,RPSFT法的MSE比BW小。随着转组率增大,两种方法的Ⅰ类错误不断升高,普遍高于0.05。与ITT法相比,RPSFT法与BW法的检验效能受转组率的影响小,下降趋势缓慢。结论:在估计试验药疗效的临床试验中,若存在对照组受试者转组到试验组的情况,统计分析以ITT法分析为主,RPSFT法和BW法作为辅助分析方法。RPSFT法和BW法相比,当截尾率和转组率均较高时,参数估计优先考虑选择使用RPSFT法。展开更多
在基于旋转不变子空间的信号参数估计(estimating signal parameter via rotational invariance techniques,ESPRIT)算法中涉及到求解信号子空间矩阵的逆矩阵,针对常用方法计算复杂度高,实时性差等问题,提出使用广义逆公式对信号子空间...在基于旋转不变子空间的信号参数估计(estimating signal parameter via rotational invariance techniques,ESPRIT)算法中涉及到求解信号子空间矩阵的逆矩阵,针对常用方法计算复杂度高,实时性差等问题,提出使用广义逆公式对信号子空间矩阵进行求解的方法.在FPGA平台上设计并实现了由复数矩阵乘法、矩阵LU分解、下三角矩阵求逆等子模块构成的广义逆矩阵求解系统.利用该系统求解广义逆矩阵所用的时间约为2.18 ms,与在MATLAB上对同样矩阵进行广义逆求解的平均用时15.7 ms减少了7.2倍.使用该系统的结果在MATLAB上完成后续仿真,对ESPRIT算法最终所得角度进行误差分析,最终所得角度的平均估计误差约为0.04°.结果表明,该系统能在保证结果精确度的同时有效减少运算时间.展开更多
The inner relationship between Markov random field(MRF) and Markov chain random field(MCRF) is discussed. MCRF is a special MRF for dealing with high-order interactions of sparse data. It consists of a single spatial ...The inner relationship between Markov random field(MRF) and Markov chain random field(MCRF) is discussed. MCRF is a special MRF for dealing with high-order interactions of sparse data. It consists of a single spatial Markov chain(SMC) that can move in the whole space. Generally, the theoretical backbone of MCRF is conditional independence assumption, which is a way around the problem of knowing joint probabilities of multi-points. This so-called Naive Bayes assumption should not be taken lightly and should be checked whenever possible because it is mathematically difficult to prove. Rather than trap in this independence proving, an appropriate potential function in MRF theory is chosen instead. The MCRF formulas are well deduced and the joint probability of MRF is presented by localization approach, so that the complicated parameter estimation algorithm and iteration process can be avoided. The MCRF model is then applied to the lithofacies identification of a region and compared with triplex Markov chain(TMC) simulation. Analyses show that the MCRF model will not cause underestimation problem and can better reflect the geological sedimentation process.展开更多
文摘目的:本文介绍等级结构保留失效时间模型(rank preserving structural failure time models,RPSFT)和BW法(the method of Branson and Whitehead)在临床试验转组研究中的应用,并对两种方法进行比较。方法:以部分对照组受试者在试验中途转至试验组为例,采用计算机模拟试验,查看不同截尾率、转组率水平下,RPSFT法和BW法估计试验药疗效的效果。以及探讨与传统的ITT法相比,RPSFT法和BW法的检验效能和Ⅰ类错误控制情况。结果:RPSFT法和BW法估计试验药疗效的准确性高,与传统的分析方法比较,引入的偏倚小。随着转组率和截尾率增大,RPSFT法与BW法的偏倚逐渐增大,估计的试验药疗效低于真实疗效,但RPSFT法的偏倚比BW法小。两种方法估计的均方误差(MSE)十分接近。当截尾率较大(40%)时,RPSFT法的MSE比BW小。随着转组率增大,两种方法的Ⅰ类错误不断升高,普遍高于0.05。与ITT法相比,RPSFT法与BW法的检验效能受转组率的影响小,下降趋势缓慢。结论:在估计试验药疗效的临床试验中,若存在对照组受试者转组到试验组的情况,统计分析以ITT法分析为主,RPSFT法和BW法作为辅助分析方法。RPSFT法和BW法相比,当截尾率和转组率均较高时,参数估计优先考虑选择使用RPSFT法。
文摘在基于旋转不变子空间的信号参数估计(estimating signal parameter via rotational invariance techniques,ESPRIT)算法中涉及到求解信号子空间矩阵的逆矩阵,针对常用方法计算复杂度高,实时性差等问题,提出使用广义逆公式对信号子空间矩阵进行求解的方法.在FPGA平台上设计并实现了由复数矩阵乘法、矩阵LU分解、下三角矩阵求逆等子模块构成的广义逆矩阵求解系统.利用该系统求解广义逆矩阵所用的时间约为2.18 ms,与在MATLAB上对同样矩阵进行广义逆求解的平均用时15.7 ms减少了7.2倍.使用该系统的结果在MATLAB上完成后续仿真,对ESPRIT算法最终所得角度进行误差分析,最终所得角度的平均估计误差约为0.04°.结果表明,该系统能在保证结果精确度的同时有效减少运算时间.
基金Project(2011ZX05002-005-006) supported by the National Science and Technology Major Research Program during the Twelfth Five-Year Plan of China
文摘The inner relationship between Markov random field(MRF) and Markov chain random field(MCRF) is discussed. MCRF is a special MRF for dealing with high-order interactions of sparse data. It consists of a single spatial Markov chain(SMC) that can move in the whole space. Generally, the theoretical backbone of MCRF is conditional independence assumption, which is a way around the problem of knowing joint probabilities of multi-points. This so-called Naive Bayes assumption should not be taken lightly and should be checked whenever possible because it is mathematically difficult to prove. Rather than trap in this independence proving, an appropriate potential function in MRF theory is chosen instead. The MCRF formulas are well deduced and the joint probability of MRF is presented by localization approach, so that the complicated parameter estimation algorithm and iteration process can be avoided. The MCRF model is then applied to the lithofacies identification of a region and compared with triplex Markov chain(TMC) simulation. Analyses show that the MCRF model will not cause underestimation problem and can better reflect the geological sedimentation process.
